The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications. Water resources management is a major challenge globally, involving tradeoffs between multiple objectives  and coordination with a heterogeneous set of stakeholders. Consequently, decision-making in the context of water resources management requires that agencies and practitioners have accurate information on water and energy conditions with as much lead time as possible. Such information is often derived from datasets being offered to end users by data producers, i.e. a producer-driven process , rather Most likely, a water resources information system would require a model that can simulate the hydrologic response at different spatial and temporal scales. Implementation of such a modeling system is hindered by the dearth of observations that can help calibrate and validate its predictions . In dataHere, we present a prototype software framework  that automates the ingestion of diverse datasets , and the deployment of a hydrologic model incorporating data assimilation and facilitating the coupling with other earth science models. This integrated system is primarily geared for easy implementation and customization requiring relatively little input from end users. Numerous modeling systems that integrate different models and observations have been developed in the past and were used as a stepping stone during the design of RHEAS. The difficulty of discovering, downloading and extracting datasets  has been previously recognized . As a reAn important aspect of a hydrologic modeling system is the internal representation of the data (input and output), with a variety of standardized file formats existing . In addition to files, data can be stored within databases (e.g. PostgreSQL) that can represent spatial and geographic objects resulting in model-independent data management. There is an advantage in using a model-agnostic storage option for the data, since that would facilitate their transferability across models  and exteData assimilation methods have been used in hydrologic research and applications to merge heterogeneous observations and models in order to improve model predictions . Such alA detailed description of the RHEAS software is given in the following section, including its architecture, different components and operating modes. A set of three example applications of the developed software framework are presented in the Application examples section, while the software status and some potential future directions are discussed in the final section of the paper.RHEAS is a modular software framework that has been developed at the NASA Jet Propulsion Laboratory (JPL) aiming at facilitating the deployment of water resources simulations and the assimilation of remote sensing observations. At the core of the system lies a hydrologic model, the Variable Infiltration Capacity model, that can be run both in nowcasting (i.e. estimation of the current time period) and forecasting (i.e. estimation for future time periods) modes. The nowcast simulation periods can be arbitrarily long, while forecast simulations depend on the length of the meteorological forecasts. In particular, seasonal forecasts will range between 1 and 6 months while long-term forecasts (e.g. climate projections) can range from 5 to 100 years. A suite of datasets from multiple sources are utilized by the system to either force or assimilate observations into the hydrologic model. Data assimilation can constrain hydrologic simulations leading to improved model states and/or parameterizations, and is explicitly incorporated within RHEAS.rheas importing the database (dbio), simulation (nowcast and forecast), and datasets modules. Users can define and perform simulations in RHEAS through a text-based configuration file with parsing functions described in the config module. Most modules need access to the database functionality, hence they import the dbio module. In addition, the simulation modules require the classes defined in the vic package to run the hydrologic model, the ensemble module to perform stochastic simulations, and the assimilation module to assimilate satellite observations.We designed the RHEAS software following a hybrid approach that combined modular and object-oriented programming. The functionality of the software was broken down into a set of components: (i) configuration, (ii) database operations (I/O and processing), (iii) model simulations, and (iv) data assimilation. Functions associated with each component were encapsulated either within a module or a class if both attributes and methods were needed to describe a component. For example, observations required both attributes  and functionality (e.g. retrieve observed variable) and therefore were represented as objects. A simplified UML component diagram is shown in datasets package , with each of being defined as a class within the  package . The PosPostgreSQL allows for querying the data by applying various operators  with arbitrary constraints. PostGIS expands these capabilities to allow processing and analytic functions , map algebra, map reprojections, and spatial resampling to coarser or finer resolutions. If the data are represented as rasters, they are split into tiles (by the PostGIS software) with each row containing a specific raster tile for each date to improve performance. Additional optimizations were implemented and tested in the database to improve query performance. Each dataset ingested in the database has different spatial resolutions, and therefore needs to be resampled to the model resolution when generating its inputs. Since the model has a set of pre-defined spatial resolutions, caching can be employed to significantly reduce query times. Resampled data can be cached as new tables, as the result of a query precomputed from operating on the original raster table. Furthermore, each database table can be clustered, i.e. its rows reordered, based on the spatial proximity of each tile allowing for quicker access to time series of the same or nearby regions.Although PostgreSQL does not support multi-threaded queries explicitly, RHEAS utilizes database connection pools (i.e. a group of independent database connections) to execute queries in parallel. precip schema. A list of the available datasets is shown in download) allowing for a common interface for the dataset functionality. Data providers use different standards to provide the datasets, both in terms of web services  and file formats . Python decorators, a metaprogramming technique, can be used to transform the dataset module’s functions during runtime and facilitate code reuse. This allows defining a dataset module using only a Uniform Resource Locator (URL) address and specifications of the URL and file types. As an example, the CHIRPS rainfall dataset that is provided as a set of Geotiff files at a web repository is fetched by simply defining the function in http decorator dynamically adds the functionality of retrieving the files defined with the url variable, while the geotiff decorator specifies how data can be extracted from the retrieved file.The dataset tables are grouped into schemas, with each schema representing a variable. For example, all precipitation-related datasets are contained within the The user can define which datasets should be ingested by creating a RHEAS configuration file that only optionally requires a bounding box, start and end dates along with the names of the datasets. If the optional arguments are not provided, RHEAS will query the database for the latest date that had been downloaded and update the dataset to today’s date. The hydrology model deployed in RHEAS is the Variable Infiltration Capacity (VIC) model . VIC solvic package, which also contains modules for saving/loading model state files (used during data assimilation) and parsing the user-provided configuration into model options.The VIC model is implemented as a class that contains the functionality of preparing the necessary input files , running the model executable, and ingesting the model output into the PostGIS database. The model class is encapsulated in the Data assimilation allows for the optimal merging of model predictions and observations by statistically taking into account the errors in both. Earth Science observations can be ingested into RHEAS using a variety of data assimilation algorithms, with the default being the Ensemble Kalman Filter (EnKF). Additional assimilation algorithms included in RHEAS are the square root EnKF (SREnKF), and the Local Ensemble Transform Kalman Filter (LETKF). The EnKF  is a varAll data assimilation algorithms require an estimate of the model uncertainty, and in the case of the aforementioned techniques that uncertainty is captured by representing the hydrologic variable to be estimated stochastically with an ensemble. Ensembles of model simulations can be generated either by perturbing model forcings and/or parameters (e.g. ), or samIn order to streamline the assimilation of multi-sensor observations, RHEAS is using an object-oriented software design maximimizing code reuse . Fig 4 sThe modular architecture of RHEAS and the ability to access data in a model-agnostic manner (via the PostGIS database) allow the one-way (i.e. offline) coupling with other environmental models by simply developing an interface against the database itself rather than the hydrologic model. As an example, an agricultural model has been coupled within the RHEAS framework enhancing the software’s applicability. The crop model included in RHEAS is based on the Decision Support System for Agro-technology Transfer (DSSAT) modeling system . DSSAT iThe DSSAT model implementation used within RHEAS is a modified version of the baseline model that can stop and restart at arbitrary times, whereas crop models generally run continuously from sowing until maturity, failure or harvest by design . This moEach RHEAS simulation begins with the parsing of a user-provided configuration file that is populated with various simulation parameters. The configuration file follows the INI format, with each section corresponding to the type of simulation (nowcast or forecast) and the model used (VIC and/or DSSAT). At a minimum the simulation configuration requires the type of model to be used, the period of simulation, a vector GIS file that defines the model domain, the spatial resolution, and a name for the simulation. Similarly, the VIC model configuration requires the source of meteorological data, and a set of output variables that are written to the RHEAS database. Additional options can be set by the user through the configuration file, although defaults have been preset in order to simplify the deployment of model simulations for non-expert users.Nowcast simulations  can eithSimilar to the nowcasting mode, forecast simulations  commenceWhenever a stochastic simulation is performed, an ensemble of model output is saved in the RHEAS database. Ensemble simulations are performed with multi-threaded processing, i.e. each ensemble member is run by a different CPU core, hence accelerating the simulation time. A byproduct of ensemble simulations is the derivation of an uncertainty estimate for each output variable, which can be expressed as the ensemble’s standard deviation. Although more sophisticated techniques of estimating uncertainty exist such as Bayesian  methods , their itesting as raster tables soil_moist and evap respectively with each row corresponding to each model time step (daily in this case). Since an ensemble simulation is performed using this configuration, a column specifying the ensemble member number will be added to the aforementioned output tables while additional tables containing rasters of the standard deviation for each variable will be added.Here, the RHEAS framework was implemented in three case study areas in order to demonstrate and evaluate its capabilities before potentially being deployed operationally, with example results described below. The Sacramento and San Joaquin river basins are located in California and cover most of the Central Valley, which is one of the most productive agricultural areas in the United States. Water resources in the region have been adversely affected by a severe drought that began in the winter of 2011 . ConsequThe simulations performed to produce these maps used the PRISM dataset to derive precipitation and air temperature forcings for the VIC model, while NLDAS was used to derive wind speed. In addition to the drought data products generated from RHEAS, uncertainty estimates for all simulated hydrologic variables were available. The Upper Colorado River plays a very important role for the water resources of the western United States , having RHEAS has been implemented over several countries in the East Africa region, with the goal of producing hydro-agricultural nowcasts and forecasts that can eventually be used to inform decision-making by farmers. 2, and a RHEAS simulation at a 25km resolution produced yield estimates during the earlier growing season of the same period as the observations. Detailed validation data are difficult to obtain, but yield observations for maize were available over the Nzoia River basin during 2000-2006. The basin has an area of 17,392 kmA software framework, RHEAS, that facilitates the deployment of water resources simulations through the ingestion and assimilation of a variety of datasets (including satellite observations) was presented along with three case studies. RHEAS has a spatially-enabled PostgreSQL/PostGIS database at the core with various datasets, both satellite and model-based, being ingested automatically. Apart from the study area, simulation period, and names of input and assimilated datasets, the user does not need to specify any additional parameters, although a programming API (in Python) allows the further customization of the system. Nowcast and forecast simulations can be performed with or without assimilating satellite observations, with the latter representing most of the water cycle components. The RHEAS database allows for the different modeling components to interface against it, simplifying the coupling of an crop growth model. Moreover, the GIS features of the database facilitate the dissemination of the RHEAS data products to diverse platforms . The output data products include an exhaustive set of hydrologic variables, with each having an uncertainty estimated associated with it.Compared to similar modeling systems that either require extensive configuration or scripting a solution custom to a specific end-user, RHEAS allows the implementation of a nowcast and/or forecasting system with minimal inputs from user automating and abstracting many of the details away. Although the case studies presented did not include extensive validation, they showcased the ability of RHEAS to generate a suite of data products in relatively diverse contexts. The spatial scales that RHEAS is applicable are governed by the hydrology model at its core, VIC, which has been implemented at resolutions ranging from 1/16° to 1°. Therefore the minimum spatial scales for the current version of RHEAS should be on the order of 5 km. Nonetheless, specific regional applications may require further calibration  of the VIC model parameters and validation against either in-situ or satellite measurements.The modular architecture and design of RHEAS could allow for potential modifications in the future that could enhance its applicability. In the context of decision-making, examples could include the use of a soil moisture change product to provide outlook on the crop growth potential, or the drought onset/duration product to plan for food storage. Apart from coupling other types of models (currently the crop growth model DSSAT is available), such as a hydrodynamic model making the system suitable for flood forecasting applications at even sub-hourly time steps. In addition, other hydrologic models can be added within RHEAS to supplement VIC creating a multi-model ensemble, which could improve predictability. Extension of the RHEAS software framework with new models will require the development of modules that implement I/O between the model and the PostGIS database, and execution of the model physics (including the preparation of its custom input files). Currently there are plans to couple additional agricultural models into the framework representing different crops  but we anticipate that code contributions from the open source community will further enhance the framework’s applicability.


Integrated navigation algorithms under the grid frame have been proposed based on the Kalman filter (KF) to solve the problem of navigation in some special regions. However, in the existing study of grid strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation algorithms, the Earth models of the filter dynamic model and the SINS mechanization are not unified. Besides, traditional integrated systems with the KF based correction scheme are susceptible to measurement errors, which would decrease the accuracy and robustness of the system. In this paper, an adaptive robust Kalman filter (ARKF) based hybrid-correction grid SINS/DVL integrated navigation algorithm is designed with the unified reference ellipsoid Earth model to improve the navigation accuracy in middle-high latitude regions for marine application. Firstly, to unify the Earth models, the mechanization of grid SINS is introduced and the error equations are derived based on the same reference ellipsoid Earth model. Then, a more accurate grid SINS/DVL filter model is designed according to the new error equations. Finally, a hybrid-correction scheme based on the ARKF is proposed to resist the effect of measurement errors. Simulation and experiment results show that, compared with the traditional algorithms, the proposed navigation algorithm can effectively improve the navigation performance in middle-high latitude regions by the unified Earth models and the ARKF based hybrid-correction scheme. More and more significant scientific research and shipping industry are carried out in the polar region . No mattThe traditional SINS based on north-oriented geographic frame performs well in middle latitude regions but loses its effectiveness in high latitude regions because of the meridian convergence . The wanThe accurate filter model, filter algorithm and correction scheme are important factors to ensure the navigation accuracy. The Kalman filter (KF) is a well-known filter method for integrated navigation applications . HoweverThe main contribution of this paper is to propose a novel marine grid SINS/DVL integrated navigation algorithm which can improve the performance of navigation system when vehicles working in or heading to the polar region. In this paper, the grid SINS mechanization with the reference ellipsoid Earth model is introduced firstly. Then, the gird SINS error equations are derived based on the SINS mechanization. According to the new grid SINS error equations, a dynamic filter model is designed and a modified accurate integrated navigation algorithm is proposed with the same Earth model. Besides, the DVL output errors are analyzed according to the DVL operating characteristic. Considering the DVL output errors, an adaptive robust Kalman filter (ARKF) is introduced, which can detect and reduce the influence of measurement errors by an adaptive factor. Then, a hybrid-correction scheme is designed based on the ARKF with a switching criterion and two correction channels. With the help of switching criterion, the proposed integrated navigation system can restrain navigation errors and improve the accuracy by choosing the proper correction channel. Finally, simulations and semi-physical experiments are conducted to validate the performance of the proposed integrated navigation algorithm.The World Geodetic System 1984 (WGS-84) Earth model is widely used as the national geodetic reference system and is also chosen as the reference ellipsoid Earth model in this paper to design the grid SINS/DVL integrated navigation algorithm. The main frames used in this paper are the inertial frame , Deriving the first order differential of Equation (15), Then, the According to Equations (17) and (20), the error equations of the Earth curvatures and twist rate in According to the grid SINS mechanization, the accuracy of When the Earth is considered as a reference ellipsoid, according to the Equations (12) and (21), the error of It can be seen that the Quaternion algorithm is usually used in the implementation of attitude update; the quaternion error differential equation in grid frame can be described as:According to Equations (25) and (26), and neglecting the second order small quantity, Equation (24) can be written as:Besides, Equation (23) can be rewritten as:According to Equation (10), the angular velocity error of Earth The attitude error equation can be calculated by Equations (27), (28) and (32):Based on the According to Equations (6), (28), (32) and (34), neglecting the second order small quantity and error of local gravity acceleration, the velocity error can be calculated as:Based on the ECEF frame, the actual position differential equation of the grid SINS can be written as:According to Equations (7) and (36), the position error can be calculated as:As shown in The local According to Equation (38), Equation (37) can be rewritten as:The DVL measures the Doppler frequency shift, and then provides three-dimensional velocity measurements. In general, the DVL has two modes of operation, i.e., the bottom lock mode and the bottom unlock mode. When the DVL is used to provide the velocity of the vehicle related to the seafloor, it operates in bottom lock mode. The main error sources of the DVL contain the sound velocity errors and the frequency shift biases. In the DVL output, the sound velocity errors mainly cause the scale factor errors and the frequency shift biases mainly lead to the random velocity errors. The DVL output in the DVL body frame , the navigation accuracy will decrease sharply when the grid SINS works independently for a long time. To ensure the navigation accuracy, an integration of grid SINS and DVL is adopted.The traditional grid frame integrated navigation systems have different Earth models between the mechanization and the filter model, which will bring the principle error and decrease the navigation accuracy.Besides, the traditional integrated navigation systems are designed by the traditional KF. The KF is an optimal state estimator based on the assumption that the system dynamic model and noise statistic characteristics are known precisely. However, the KF would not be optimal when used in practice due to the uncertain measurement noises and outliers.Finally, the traditional integrated system with the output-correction or the feedback-correction has its own shortages. The output-correction corrects the SINS output errors directly. The traditional integrated system with the output-correction can converge with a fast speed when the measurement contains mutation errors, but the SINS inner errors are not corrected and still accumulate with time. Due to the accumulated error, when working for long hours the filter state equation will not match the actual SINS working state and the filter accuracy will decrease or even diverge. The ships or submarines always work for long hours in or heading to the polar region, so the output-correction is hardly suited for the navigation task. In contrast, the feedback-correction can correct the SINS inner errors with the same update frequency of the filter, but the filter output will be involved in SINS calculation. As a result, the filter errors become one of the SINS error sources. When the measurement contains unexpected mutation errors, the feedback-correction system will converge slowly compared to the output-correction and the filter estimation results will contain serious errors. If the serious errors are introduced into the SINS, the navigation accuracy will decrease seriously and the influence will exist for a long time. In practice, the uncertain DVL output errors are unavoidable, so the feedback-correction is not a proper correction method.To overcome the disadvantages of the traditional system discussed above and improve the navigation performance for marine applications, a novel grid SINS/DVL integrated navigation algorithm is designed with the unified Earth model and an ARKF based hybrid-correction scheme is proposed in the following sections.The dynamic models of the integrated navigation system include grid SINS model and DVL model. Assuming that constant errors , the DVL output in The grid SINS output velocity can be described as:The observation model of integrated system can be given by:Because the DVL output errors with imprecise or unknown statistics are hard to be modeled, an ARKF is employed to restrain the uncertain measurement errors and improve the filter accuracy. The adaptive factor is used in the ARKF algorithm to adjust the filter algorithm. The ARKF can detect and reduce the measurement mutation random errors.The ARKF is based on M-estimation, and the filter steps are described as the follows ,29:(55)XThe institute of geodesy and geophysics (IGG) III function of the adaptive factor ained by ,29:(60)αThe equivalent covariance of the measurement noise is related to the DVL measurement, and the equivalent covariance ained by ,29:(61)RWhen the ARKF is applied to the grid SINS/DVL integrated navigation system, measurement outliers will be isolated or weighted less to enhance the accuracy of navigation system.In this paper, considering the grid SINS/DVL integrated system character, an ARKF based hybrid-correction scheme is proposed to improve the navigation accuracy.The ARKF based hybrid-correction scheme contains three parts, i.e., an output-correction channel, a feedback-correction channel and a two-order switching criterion. The ARKF based hybrid-correction integrated navigation structure diagram is shown in A certain correction period is given by the empirical value. As shown in The two-order switch criterion is used to choose the output-correction channel or the feedback-correction channel, which is shown in As shown in To validate the necessity and advantages of the filter model with unified Earth model, the ARKF algorithm and the ARKF based hybrid-correction scheme, the simulations and experiments are conducted in middle and high latitude regions respectively.To estimate the performances of the designed algorithm, the sea state and vehicle motion status are also considered. The sea state is set as moderate condition, and the motion status includes static, motion with constant velocity, motion with constant acceleration, climbing and turning a corner. The main IMU and motion parameters of the grid SINS trajectory generator are described as follows.−5 and the random errors of accelerometers are set up as zero-mean Gaussian white noises.Firstly, the gyroscopes drifts are set as 0.03° per hour and the random errors of gyroscopes are set up as zero-mean Gaussian white noises. The accelerometer biases are set as 5 × 10Secondly, the attitude of the vehicle is set as a sine function to simulate the influence of the moderate sea state. The amplitude and period (denoted as amplitude/period) of pitch angle, roll angle and yaw angle are set as Thirdly, the acceleration of the vehicle is set as Finally, the DVL output errors are set up according to the different experiment requirements, which will be illustrated in the following sections.The simulation experiments compare different filter models, correction schemes and filter algorithms.There are two integrated filer models based on two different grid SINS error equations discussed in this section. The first model assumes the Earth as a sphere and the second model proposed in this paper assumes the Earth as a reference ellipsoid. Other parameters of the two methods are as follows: the DVL output errors are set up as the zero-mean Gaussian white noises. The KF is chosen as the filter algorithm. The output-correction is chosen as correction scheme and the simulation time is set up to 8 h.It is noted that the integrated navigation system based on the two filter models can both restrain the errors of grid SINS, but the proposed model can unify the Earth model of the SINS mechanization and the integrated filter. The simulation experiment is to compare the navigation performance of integrated filter models and choose the better one.The navigation errors in middle latitude regions are depicted in The navigation errors in high latitude regions are depicted in As shown in As shown in The simulation results show that the proposed reference ellipsoid based filter model can provide a better accuracy than the traditional filter model because of the unity of Earth models.There are three correction schemes discussed in this section: the output-correction, the feedback-correction and the hybrid-correction. All three correction schemes can be used in the grid SINS/DVL integrated navigation system to resist SINS navigation errors. The former two schemes are traditional methods and the third scheme is the one proposed in this paper. The simulation experiment is to compare the performance of the three correction schemes.In this simulation, the DVL output errors are set up as zero-mean Gaussian white noises. The reference ellipsoid based model is chosen as the integrated filter model which has been discussed above. The KF is chosen as the filter algorithm, which means the hybrid-correction only have the first order time criterion. The simulation time is set up to 24 h.The navigation errors in middle latitude regions are depicted in The navigation errors in high latitude regions are depicted in As shown in The navigation error curves of the hybrid-correction system are shown in Therefore, the grid SINS/DVL integrated navigation system with the hybrid-correction scheme has a higher navigation accuracy and better performance than traditional correction schemes.The simulation experiments mentioned above all assume that DVL output errors are zero-mean Gaussian white noises, and the filter algorithm is the KF. However, when the DVL operates in practice, the DVL output may contain different kinds of errors, such as the normal DVL output noises, the unpredicted large random errors, the velocity of the ocean current, etc. In this case, the KF will not work in optimal state. To illustrate the filter algorithm, the DVL output errors in trajectory generators are adjusted to simulate the errors in practice.There are two integrated filter algorithms discussed in this simulation experiment. The reference ellipsoid based filter model is chosen as the integrated model which has been discussed above. The filter algorithms are the KF and ARKF, respectively, so the correction schemes are the KF based hybrid-correction and the ARKF based hybrid-correction, respectively. When the DVL fails to maintain bottom lock in short time, the velocity of ocean current will be considered as a constant. The DVL output errors are shown in When the DVL works properly, the errors are set up as zero-mean Gaussian white noises. When the DVL fails to maintain bottom lock during The errors of the grid SINS/DVL integrated navigation in middle latitude regions are depicted in The errors of the grid SINS/DVL integrated navigation in high latitude regions are depicted in As shown in During the first one hour, the DVL works properly, the KF and ARKF have the same performance.If the KF is chosen as the filter algorithm, large oscillation errors appear in the velocity information when the DVL fails to maintain bottom lock. At the same time, large oscillation errors and accumulated errors appear in the position information. The KF is sensitive to measurement noises and unable to restrain measurement outliers. The DVL constant errors and large amplitude random errors will increase the filter errors. When the large filter errors are introduced into the grid SINS through feedback channel, it will lead to the decrease of the navigation accuracy. Besides, the DVL constant errors have a larger influence to the navigation accuracy than large amplitude random errors.If the ARKF is chosen as the filter algorithm, the oscillation amplitudes of velocity and position are much smaller than the KF based system and the longitude error barely accumulates with time. The reason is that the ARKF employs an adaptive factor to detect and reduce the influence of measurement uncertain errors. Meanwhile, when the adaptive factor detects a large measurement error, the switching criterion of the ARKF based hybrid-correction can reject the feedback-correction channel to avoid filter errors to be introduced into the grid SINS. Therefore, the ARKF based hybrid-correction system has a higher accuracy and stronger robustness than the traditional KF based hybrid-correction integration.Due to the geographic restriction of the field experiment especially the polar experiment, a semi-physical experiment is conducted in this paper. The key point of the semi-physical simulation experiment is to get the actual sensor output errors, including the IMU output errors and the DVL output errors. The actual sensor errors can reflect the actual error characteristic of the sensors. Meanwhile, the grid based SINS trajectory generator can provide the ideal sensor output data. Then, a set of semi-physical experiment sensor output data can be obtained by adding the actual sensor errors to the ideal sensor output. The semi-physical data can reflect the performance of the navigation algorithm more properly when it is used in experiments.A turntable experiment is designed to extract the IMU output errors. An IMU is installed on a high-precision three-axis turntable shown in The IMU actual output data, i.e., The actual output of the gyroscope and accelerometer can be presented as:Then, the actual IMU output error can be obtained by:In the semi-physical integrated navigation experiment, the IMU error data A ship-mounted experiment is carried out in the Dalian sea area. The DVL error characteristic is susceptible to the vehicle motion status and the working environment, and a set of DVL actual output errors is extracted according to this experiment.A high precision SINS/GNSS integrated navigation system and a DVL are mounted on a ship and both can provide ship velocity, respectively. The main parameters of the SINS/GNSS navigation system and DVL are shown in The ship trajectory in this experiment is shown in The output velocity of the SINS/GNSS integrated navigation system is donated as Then, the DVL output errors can be calculated by:The DVL output errors are shown in As shown in The DVL error data The ideal sensor output data have been generated by the grid trajectory generator, including the gyroscopes output There are three different kinds of grid SINS/DVL integrated navigation algorithm compared in this experiment. The former two algorithms are traditional methods. These two traditional algorithms choose the sphere as the Earth model in KF filter models, which means that the integrated system operates with different Earth models between the grid SINS mechanization and the integrated filter. The first traditional algorithm chooses the output-correction scheme and the KF, and the second traditional algorithm chooses the feedback-correction scheme and the KF. The third algorithm is the ARKF based hybrid-correction grid SINS/DVL integrated navigation algorithm proposed in this paper, which chooses the reference ellipsoid as the Earth model to unify the Earth model of the grid SINS mechanization and the integrated filter.The errors of the grid SINS/DVL integrated navigation in middle latitude regions are depicted in The errors of grid SINS/DVL integrated navigation in high latitude regions are depicted in As shown in Compared with the traditional output-correction, the KF feedback-correction algorithm corrects the SINS inner errors in every filter period and the Schuler oscillation is completely suppressed. The attitude and position accuracies are higher than the output-correction system. Due to the weakness of the position observability , the posThe proposed ARKF hybrid-correction system has the best performance among the three methods. Compared with the traditional KF output-correction system, the ARKF hybrid-correction system has a higher navigation accuracy. Compared with the traditional KF feedback-correction system, the proposed ARKF hybrid-correction system has the similar attitude accuracy but higher velocity and position accuracy and stronger robustness. The reasons are as follows: firstly, the unified Earth model decreases the principle errors; secondly, the navigation output errors are corrected by the output-correction channel of the hybrid-correction; thirdly, the SINS inner errors are corrected periodically by the feedback-correction channel of the hybrid-correction to ensure the navigation accuracy when working for long hours; fourthly, the ARKF filter can detect and adjust the filter algorithm to restrain the uncertain DVL output errors; and, finally, when the DVL output contains large noises, the residual filter errors after ARKF are rejected with the help of the A novel grid SINS/DVL integrated navigation algorithm is proposed to improve the performance of integrated navigation systems in middle-high latitude regions. The grid SINS error equations based on reference ellipsoid are firstly derived, and a more accurate integrated navigation filter model is designed with the unified Earth model to improve the integrated navigation accuracy. To resist the effects of measurement errors, the ARKF is employed and the ARKF based hybrid-correction scheme is proposed, by which the navigation performance is further improved and ensured for a long working time. The results of simulation and semi-physical experiments show that the proposed grid SINS/DVL integrated algorithm can effectively improve the navigation performance even working for long hours both in middle and high latitude regions. In the future, more kinds of external measurements will be employed to improve the integrated navigation accuracy and reliability.


A proof-of-concept scenario with three sensors was performed, including the manufacturing of the encoded sensors. Furthermore, an interrogation setup to detect the sensors central wavelength was proposed and its working principle was theoretically developed. Results show that total identification of the central wavelength is performed under spectral overlapping between the manufactured sensors, achieving a three-time improvement of the system capacity. Finally, the error due to overlapping between the sensors was assessed obtaining approximately 3 pm, which makes the approach suitable for use in real measurement systems.In this paper, an encoding strategy is used to design specialized fiber Bragg grating (FBG) sensors. The encoding of each sensor requires two binary codewords to define the amplitude and phase patterns of each sensor. The combined pattern (amplitude and phase) makes each sensor unique and therefore two or more sensors can be identified under spectral overlapping. In this way, we add another dimension to the multiplexing of FBG sensors, obtaining an increase factor ‘ Optical fiber sensing technologies take advantage of optical fiber properties such as immunity to interferences, low losses, and light weight, among others . Therefoechnique . By simpechnique ,5,6,7. Aechnique ,9,10, inn’ by using ‘n’ codewords. We take advantage of the technological readiness to manufacture super-structured FBG devices with complex index profiles in phase and amplitude. In fact, we have previously demonstrated the possibility of manufacturing amplitude-encoded FBG sensors with orthogonal properties between them to achieve an enhancement in the number of sensors of a conventional interrogating scheme. Here, we propose the manufacture of specialized FBG sensors encoded using their amplitude and phase terms. The design of the proposed sensors is customized to fit the dual wavelength interrogating setup presented in . This codeword would be translated to a SSFBG device composed of five equidistant wavelength slots featuring reflection sub-bands only in the second and fifth positions (corresponding with the 1’s in the codeword). Phase information can also be assigned to each active sub-band, i.e., each reflection sub-band is able to phase-shift the reflected waves by a fixed value, with the property that the relative phase between two consecutive sub-bands is fixed to a discrete value  is through comparison of the phase value between two adjacent sub-bands, obtaining the relative phase-shift through comparison to a local oscillator. This is the reason why single-sideband (SSB) modulation of the source is performed, obtaining a tunable dual-wavelength source interrogation system. The key of this interrogation scheme is that the dual-wavelength source separation matches the distance between sub-bands in the sensing device, so that the relative phase between two adjacent sub-bands in the sensing network can be measured. This makes it possible to distinguish whether or not two adjacent sub-bands introduce a phase shift.interrogated sub-band for each consecutive pair of sub-bands in the actual SSFBG device, with a relative phase value depending on the phase relationship between sub-bands in the original device. The interrogation process is depicted in From the establishment of super-structured FBG sensors and their interrogation methodology discussed above, we move now to describe the design of mutually orthogonal super-structured FBG sensors.K encoded SSFBG sensors.kth sensor is described not only in terms of the number of sub-bands N and the codewords The first consideration to get into the design of the SSFBG sensors has to do with the practical parameters needed to manufacture the devices. Each SSFBG sensor shape in the network is established by a number of parameters that can be optimized either to facilitate their detection or to adapt the sensors behavior to the network characteristics . The main descriptor parameters in our SSFBG sensors are of course the amplitude and phase codewords that define sub-band distribution. Still, other important design parameters are the spacing between slots In order to obtain a set of sensors mutually orthogonal with each other we take some concepts from optical orthogonal codes (OOC), which are binary codewords used in optical code division multiple access (OCDMA) communication systems; in our previous work, OOCs were used to shape the amplitude pattern of a SSFBG device. Here, we use the same concept with the addition of phase encoding to the reflecting sub-bands. This is represented in Equation , by the A set of three amplitude and phase codewords is selected as exemplified in kth sensor in the lower sub-band The interrogation method was described briefly in the Ein(t)=AaEin(t)=AaEquation :(3)Es∝HaIn a ‘square-law’ detector, the detected photocurrent is proportional to:So, from Equations  and 4)  4) the eFirst, we have the beating terms falling in the baseband region; these are represented in Equation set (5)direct terms to those terms created by the two interrogating bands being reflected from a unique sensor, they are expressed in Equation set (6).Then, we will call cross terms; they are represented in Equation set (7).On the other hand, the terms proceeding from two different sensors beating the two wavelengths are called the direct and cross terms, we need to compute the different products .In order to develop the Terms in Equation (8) represent the interaction between the dual-wavelength source and the sensing network for two generic wavelengths direct and cross terms, which are determined by the phase relationship between adjacent sub-bands of the K sensors present in the network  laser beam exposure using the phase mask (PM) method to produce the compounded Bragg pattern. Hence, the optical fiber was exposed twice for each sampling point, to a UV beam focused to a diameter of ∼80 ve index . The manribed in .It is important to note, in Yenista TUNICS T100R) was swept over the operating range of the sensors at 1 nm/s. During the sweeping, the laser was SSB modulated at 10 GHz to get the dual-wavelength interrogation signal.After successful manufacturing of the sensing devices. The experiment with three overlapping sensors was carried out and the setup is the one proposed in The dual-wavelength source triggers the VNA to measure in time the complete network frequency response. Additionally, an optical spectrum analyzer (OSA) is used to monitor the interrogating signal.The sensors are stabilized in temperature and strain shifted in order to create the overlapping scenario. At each point of the experiment, the readout from the sensing network is captured at the VNA and stored in the computer to get each sensor central wavelength.As mentioned in The last stage of the experimental validation consists in the assessment of the error induced to the system when we add a number of sensors that actually share the same spectral range. In order to characterize the amount of induced error, we need to remove the effect of the additional sensors interfering within the readout. Consequently, we performed an experiment that recreates the overlapping scenario, so that we could measure both the individual contribution from each sensor and the compounded readout when two or three sensors are interacting with each other. In this way, we also neglect the human error from the measurements since we are taking the individual and compounded contributions at the same instant along the same experiment.Hence, the individual wavelength was measured for the sensing device In this paper we have proposed an encoding technique to add a multiplexing dimension to the traditional WDM approach in FBG sensing networks and enhance the number of sensors that can be deployed. The encoding approach proposed here is intended to add information to each sensor by modifying its amplitude and phase profiles. Therefore, we matched the design of the sensor’s amplitude and phase profiles with the interrogation technique in such a way that they complemented each other. The interrogation technique uses a dual wavelength source and a vectorial network analyzer to retrieve the differential profile from each sensing device in the network. This technique and its interaction with the sensors was theoretically developed in order to better understand the measured terms from the network frequency response.Taking advantage of the possibilities of manufacturing specialized super-structured FBG devices, we fabricated the proposed encoded sensors with very specific profiles in amplitude and phase. This allowed us to perform an experimental validation of the proposed technique, showing that the system is capable of identifying each sensor wavelength even when three sensors are merging into each other spectrally. We also addressed the error added to the network due to the existence of overlapping between two or three devices. This additional error has a very low impact in the measurement. The worst case of standard deviation was 3.9 pm, when three sensors were sharing the same spectral region. We showed that the off-line detection procedure averages the ripple frequency caused by overlapping sensor sub-bands. Future work will involve the development of additional off-line procedures to decrease the cross-talk noise gain due to the interference between sensors.


Being active in sports has many positive health effects. The direct effects of engaging in regular physical activity are particularly apparent in the prevention of several chronic diseases, including cardiovascular disease, diabetes, cancer, hypertension, obesity, depression, and osteoporosis. Besides the beneficial health effects of being active, sports participation is unfortunately also associated with a risk of injuries.In the case of many sports injuries  preventive measures are not compulsory, which means that a behavioral change in sports participants is necessary to increase the use of effective measures, and subsequently prevent or reduce injuries in sports.The evidence-based Wintersportklaar online intervention has been developed to stimulate injury preventive behavior among skiers and snowboarders. In this article, the design of the effectiveness study will be described.T tests and/or Mann-Whitney test). Chi square tests and/or logistic regression analyses are used to analyze behavioral change according to the intention to treat principle .A randomized controlled trial with a follow-up period of four months during the winter sport season will be conducted. The participants consist of inexperienced skiers and snowboarders. At baseline, skiers and snowboarders in the intervention and control groups are asked to report the injury preventive measures they usually take during their preparation for their winter sport holiday. One and three months after baseline, skiers and snowboarders are asked to report retrospectively in detail what measures they took regarding injury prevention during their current winter sport preparation and winter sport holiday. Descriptive analyses  are conducted for the different baseline variables in both study groups. To evaluate the success of the randomization, baseline values are analyzed for differences between the intervention and control groups  Skiing and snowboarding are very popular winter sports worldwide, including among Dutch residents. Nearly one million Dutch snow sport fanatics travel each year to the mountains to spend their winter holidays in the snow, with Austria, France, Germany, Switzerland, and Italy being the most popular destinations . BesidesKnown risk factors for the occurrence of snow sport injuries are snow sport experience, rented equipment, weather conditions, speed, fatigue and technical errors ,6. The dAccordingly, a scientific research project has started in the Netherlands . The aims of this project are: (i) to develop an evidence-based intervention to stimulate injury preventive behavior among skiers and snowboarders; and (ii) to evaluate the effectiveness of the developed intervention. The effectiveness of the developed intervention will be evaluated through a randomized controlled trial. This article describes the design of this study.The Consolidated Standards of Reporting Trials (CONSORT) statement is followed to describe the design of the study . This stThe objective of the study is to evaluate the effectiveness of the Wintersportklaar intervention  on injury preventive behavior among skiers and snowboarders. The hypothesis of this study is that the developed intervention would lead to a 10% increase in favorable injury preventive behavior in the intervention group in comparison to the control group.A randomized controlled trial with a follow-up period of 4 months during the winter sport season will be conducted see . The stuThe participants consist of inexperienced skiers and snowboarders. Inclusion criteria are: (i) less than five weeks of winter sport lessons or; (ii) less than two weeks of winter sport holiday per year or; (iii) less than five weeks of winter sport experience or; (iv) low physical fitness. An exclusion criterion is cross-country skiing.Because of the relatively short duration of the winter sport season, participants can preregister 2 months before the start of the study. Participants are recruited via social media networks  of the participating organizations (Dutch Consumer Safety Institute and Dutch Skiing Association). All the preregistered participants will receive the baseline questionnaire.The study protocol has been approved by the Medical Ethics Review Committee of the Academic Medical Center . Participants willing to take part in the study have to give their informed consent prior to the start of the baseline questionnaire. The flow of the participants is presented in In this study, the assumption is made that a 10% increase in favorable injury preventive behavior in the intervention group compared with the control group can be achieved. To achieve 80% power with a significance level of 0.05, and taking into account a potential loss to follow-up among participants of 10% over the winter sport season, the sample size calculation reveals that 423 skiers and snowboarders per study group are needed in this study. Eligible participants will simultaneously be allocated at random to the intervention or control group after T0, using a computerized random number generator (the ASELECT function in Excel). No restrictions will be imposed for the allocation; simple randomization will be performed. All steps in the randomization process will be done by 1 researcher.The Wintersportklaar intervention has been developed according to the steps of Intervention Mapping (IM) and Knowledge Transfer Scheme -10. The Wintersportklaar will be made available exclusively to the participants within the intervention group, and is an evidence-based intervention developed to stimulate injury preventive behavior among skiers and snowboarders. The development of the intervention was a collaboration between snow sport experts and the target group. According to the Dutch winter sport experts who were consulted, the skill level and the physical fitness level of skiers and snowboarders are the two main dimensions involved in the risk of winter sport injuries.Part one of the intervention is a short questionnaire. Based on 8 single questions, the physical fitness and skill level of the skiers and snowboarders will be determined and classified as: 1) low level of physical fitness and skill level; 2) low level of physical fitness and sufficient skill level; 3) sufficient level of physical fitness and low skill level; and 4) sufficient level of physical fitness and skill level. Depending on their answers, skiers and snowboarders will receive tailored advice on optimal preparation for their winter sport holiday (part two). Examples of advice include taking winter sport lessons, and strength exercises. Furthermore, advice about the use of protective gear and the 10 Fédération Internationale de Ski (FIS) rules for the conduct of winter sport safety are given. After receiving the tailored advice, the skiers and snowboarders can leave their email address on the website in order to receive a personal exercise schedule every week until the start of their winter sport holiday (part three). The Wintersportklaar intervention is made available to the participants within the intervention group. Huge efforts have been made to develop an easily accessible intervention , with a low threshold, and easily applicable injury preventive advice.The skiers and snowboarders in the control group do not have access to the intervention and prepare their winter sport holiday as they normally would.In our study, our main outcomes measure injury preventive behavior defined as: (i) taking winter sport lessons; (ii) performing strength exercises; (iii) the use of protective equipment; and (iv) knowledge of the 10 FIS rules for the conduct of safety in winter sport. Each injury preventive topic is divided into preparatory acts like searching for information about injury preventive measures, buying a winter sport helmet, etc, and structural injury preventive measures . Taking winter sport lessons consists of two preparatory acts and three structural injury preventive measures. Performing strength exercises consists of two structural injury preventive measures, and the use of protective gear consists of three preparatory acts and three structural injury preventive measures. Finally, knowledge of the 10 FIS rules for the conduct of safety in winter sport consists of three preparatory acts. All injury preventive behaviors are assessed through single questions .At baseline, respondents will be asked about their winter sport demographic characteristics, winter sport skill level, physical fitness, current injuries and physical complaints, winter sport preparation, and winter sport injury preventive measures. Furthermore, skiers and snowboarders in the intervention and control group will be asked to report the injury preventive measures they usually take during their preparation for their winter sport holiday. One month and three months after baseline, skiers and snowboarders will be asked to report retrospectively in detail what they had done up until then regarding injury prevention during their current winter sport preparation and holiday. In the follow-up measurements, respondents will be asked about any injuries they suffered during their winter sport preparation and holidays. In our study, an injury is defined as an event during skiing or snowboarding after which the participants have to stop their winter sport activities or are unable to participate in another winter sport activity -15).For all three measurements, an online form will be sent by email by 1 researcher through SurveyMonkey, an online questionnaire system (www.surveymonkey.nl). The first reminder will be sent after 1 week if no response is received. A second reminder will be sent after 2 weeks if no response is received.The intervention group will be additionally questioned about their use and valuation of the Wintersportklaar intervention. The evaluation of its effectiveness will not take place in an experimental setting; participants in the intervention group will be given access to the intervention, but no further conditions are applied to the use of the intervention. In the questionnaires given at one month and three months, the intervention group will be asked about their use of the intervention, the appeal of the intervention, the behavioral actions they took after being exposed to the intervention, their intention to use the intervention again, and how they would want to be informed about the intervention. If participants indicate that they do not want to use the intervention, they will be asked to explain their decision in detail.T tests and/or Mann-Whitney test).Descriptive analyses  are conducted for the different baseline variables for both study groups. To evaluate the success of the randomization, baseline values are analyzed for differences between the intervention and control groups .Chi square tests and/or logistic regression analyses will be used to analyze behavioral change according to the intention to treat principle. According to this principle all participants who were enrolled and randomly allocated to the intervention or control group are included in the analysis and are analyzed in the groups to which they were randomized. In a secondary analysis, behavioral change is analyzed according to the non-intention to treat principle. The proportion of injured winter sport participants is calculated by dividing the number of injured respondents by the total number of respondents in this study and per study group. Differences in the proportion of winter sport injury between the intervention and control groups are assessed using logistic regression analysis . In a previous study, we also used social media to recruit skiers and snowboarders. Within two weeks, we received 300 responses, without the use of any rewards for participants or paid advertisements on social media. For this study, respondents will be offered a chance to win a money prize if they complete all three questionnaires. The chance to win a reward (€250) is used to enhance the willingness of skiers and snowboarders to participate in the study and the respondents’ adherence to the study, even after their winter sport holidays are over. In combination with the possibility to preregister for the study and the use of paid advertisements on , we believe that the recruitment of 848 skiers and snowboarders will be challenging, but feasible.Another challenge is the use of injury preventive behavior and/or a change in injury preventive behavior as a primary outcome measure in a study regarding injury prevention rather than winter sport injuries. Clear definitions of injury preventive behavior are necessary, as well as extensive measurements of injury preventive behavior at baseline and subsequent measurements. Furthermore, we have to determine what a relevant change in injury preventive behavior is. In this study, we limit injury preventive behavior to taking winter sport lessons, performing strength exercises, the use of protective equipment, and knowledge of the 10 FIS rules of conduct. To enhance the measurement of injury preventive behavior, each injury preventive behavior topic will be operationalized in preparatory acts and structural injury preventive measures. To better detect change in our measurements, we use dichotomous answer categories instead of, for example, a 5-point scale. Finally, it is well-known that changing health-related behavior is difficult . It is a


In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size. Recently, elevators for convenience of movement and transportation have become essential facilities inside of buildings, particularly with the development of high-rise buildings, and the installation of elevators has been rapidly increasing all over the world. In addition, cranes and lifts are essential pieces of equipment for transporting materials on construction sites.In various industrial facilities, steel wire rope, which has a high strength and high flexibility, is a key mechanical element used for power transmission and is widely used because it has the advantages of reliability and efficiency.Wire ropes fully support the load of structures or cargo, so damage to a wire rope can lead to great risks. However, local cross-sectional damage in a wire rope can occur due to aging caused by long-term use, corrosion caused by the external environment, damage due to unexpected mechanical movement, and local defects due to friction with peripheral devices, etc. These small defects can expand quickly because of the tension in the wire rope, which can lead to the lifting structure falling apart or other structural failure. However, such damage is not easily detected due to certain properties of the wire rope, such as its complicated cross section and long length; and thus some wire ropes are being used in very dangerous conditions in situ ,2.Additional, due to the features mentioned above, the remaining service life of wire ropes cannot be accurately predicted, so the ropes are regularly replaced. In particular, it is estimated that more than 70% of ropes have been replaced despite the fact that there is no problem with the strength, and there is a recurring economic loss as a result. Thus, it is very important to detect initial defects in wire ropes at the early stages to prevent both accidents and avoid economic losses.Currently, wire rope inspection relies almost entirely on manual inspection methods along with visual inspection. This requires a lot of time and money, and there is a high probability of human error because it is diagnosed is based on an individual’s decision. Visual inspection also has a fatal disadvantage in that it cannot inspect local flaws or internal corrosion.To overcome these drawbacks, non-destructive testing has recently been introduced to check the state of wires, but only up to a limit. Techniques such as radiographic testing and ultrasonic testing (referred to as representative non-destructive testing methods) have the potential to reduce the risk of radiation exposure, but they are associated with low inspection efficiency and problems with wave propagation due to the geometry of the complicated wire ropes .Therefore, magnetic testing can be a very useful method for detecting defects in wire ropes because such wire ropes are ferromagnetic materials that are easily magnetized, and most non-destructive testing methods for wire rope inspections primarily use magnetic sensors. In this method, a magnetic field is applied to a wire, and a magnetic sensor detects any changes in the magnetic flux or leakages of the magnetic field from a defective portion of the wire ,4,5.Magnetic sensors have the advantages of excellent reliability and reproducibility, and excellent diagnostic performance can be expected when combined with signal processing technology ,7,8,9.Magnetic sensors vary in type and are used in accordance with the characteristics of the target structure considering any possible damage . Wire roConsidering these characteristics, the magnetic flux leakage (MFL) method was selected as a suitable test method for wire rope in this study. This is because the MFL method has been effectively applied to diagnose local damage in continuous ferromagnetic structures such as pipes and rails, and it has the advantage of high-speed non-contact diagnosis ,12,13.Currently, most wire rope diagnostic techniques using the MFL technique employ a one-dimensional raw magnetic flux signal. However, there is a restriction in that the damage must be judged via subjective signal analysis by a professional, and thus such methods are not widely used.To overcome these limitations, this study aimed to develop an automatic damage assessment method based on MFL that quantitatively assesses the degree of damage by objectively determining whether or not the damage has occurred automatically without expert intervention.To accomplish this, magnetic flux data measured from 8-channel MFL sensor heads were processed by filtering and the Hilbert transform method, outlier analysis was performed by comparing the results with a threshold using the GEV distribution, and the existence of damage was objectively determined.Assuming damage was identified, damage indexes were created to quantify the MFL signals by extracting the magnetic characteristics of the MFL signals. Finally, an artificial neural network (ANN)-based pattern recognition technique using extracted damage indexes was developed, and the size of the damage in the wire rope was automatically and objectively estimated.Any magnetized ferromagnetic material can be considered as a magnet. The magnetic field spreads out when it encounters a small air gap created by the defect, as the air cannot support as much magnetic field per unit volume when compared to the magnet. When the field spreads out, it starts to leak out of the material, and this is called magnetic flux leakage. Before measuring the magnetic flux leakage, specimens must reach saturated magnetization conditions by applying a field large enough to cause the magnetic flux to effectively leak out. To establish the magnetic flux in the material to be inspected, a strong permanent magnet is used to magnetize the specimen in this study. When no damage is present, the magnetic flux in the specimen remains uniform, as illustrated in Magnetic flux leaks out of the metal specimen near the damaged areas. Sensors that can detect this flux leakage are placed between the poles of the magnet. The sensors then generate a voltage signal proportional to the magnetic flux leakage ,14. In tB) is applied to a plate, an electron moving through the magnetic field experiences a force, known as the Lorentz force, which is perpendicular to both to the direction of motion and the direction of the field. The response to this force then creates a Hall voltage [When a magnetic field /σ > 0, where μσ > 0 is the scale parameter, and ξAfter obtaining the magnetic flux signal, the appropriate threshold that distinguishes between the normal and damaged conditions needs to be established. In this study, a 99.99% confidence level threshold of the normal condition was established using the generalized extreme value (GEV) distribution. The GEV distribution is the limit distribution of the properly normalized maxima of a sequence of independent and identically distributed random variables according to the extreme value theorem . The GEVarameter . When thTypically, two kinds of damage indexes that can be extracted from raw MFL signals have been used to quantify the MFL signal to estimate the amount of damage ,18.V) shown in W), as shown in The peak to peak value ’ as shown in First, the maximum peak of the enveloped signal that exceeds the threshold was extracted and was named the ‘peak value of envelope (EW) was extracted to supplement the P-P width by calculating the range where the envelope exceeds the threshold, which was then used to represent the x-component of the enveloped MFL signal, as shown in W extracted from under the peak is generally larger than the P-P width, in accordance with the triangular shape [Also, the width of the envelope  was applied to stably represent the width of the peak. To extract the FWHM, the width value at half height of the peak was indexed when the peak was obtained from a signal, as shown in However, when the peak is too large or too small, EA) was extracted by integrating the amplitude of the signal in the excess range considering, the shape of the envelope signal, as shown in In addition, the area of the envelope  can be used to make an approximation function by learning from collected data, and it can objectively classify unknown data based on this approximation function . The ANNA supervised multi-layer feed-forward ANN with backpropagation is typically employed. The Levenberg-Marquardt (LM) algorithm, which is similar to the Newton method , is usedA series of experimental studies was carried out to examine the capabilities of the proposed damage detection and quantification technique. To perform the experiments, five steel wire-rope specimens with a 10 mm diameter and 800 mm length were prepared. An intact wire rope was prepared and magnetic flux signals were measured from this wire rope, which were then used to establish the threshold for the outlier analysis. Four kinds of artificial damaged steel wire specimens were prepared for quantitative analysis according to the size of the damage, as shown in As shown in In other words, the change in MFL signal was determined according to the change in damage width. The changes in MFL signal were also analyzed for various damage depths, which was accomplished by comparing the wire ropes (#1–#4) with different depths. The test setup for measuring MFL signals was composed of an MFL sensor head, a compact DAQ, and a terminal board, as shown in The MFL sensor head moves linearly along the fixed wire rope specimen with a constant velocity of 2 m/s to measure MFL signals using a linear moving machine. The data acquisition equipment, which consists of a terminal board and a compact DAQ, measures 8 magnetic flux signals simultaneously at the MFL sensor head.The MFL sensor head is composed of 8 channels of the sensor module, and each sensing module contains a Hall sensor and a permanent magnet yoke that can independently measure the magnetic flux signal. Eight sensing modules were circumferentially arranged in a circular configuration to measure signals from the entire cross section of the wire rope, as shown in Signals were repeatedly measured 40 times at each specimen, and the sampling rate was 10 kHz. The measured signals were then processed to facilitate effective damage detection throughout signal processing and the enveloping process based on the Hilbert transform (HT).After signal processing, the enveloped MFL signals measured from wire ropes #1–#4 are displayed by overlapping in The most sensitive sensing channel for upper damages is channel 3, which is nearest to the upper damage, and channel 7 is most sensitive for lower damages. A relatively weaker MFL signal was detected in the neighboring sensing channels. This shows that the closer the damage is to the sensing channel, the better the detection capability of the signal, indicating that the circumferential direction of the damage can be deduced using this method.MFL signals collected from wires #1–#4 with different depths of damage were compared to analyze the change in patterns of the MFL signals with increasing damage depth. In this research, a quantitative analysis was conducted using the MFL signals measured from the top four damaged regions by utilizing the most sensitively measured signals from channel 3.It can be seen from V), is presented in Next, the changes in envelope signal were quantitatively extracted according to the damage depth by using the damage indexes introduced in P), which is similar to the P-P value, steadily increased with increasing damage depth.W), which is a damage index developed to estimate the original damage width. The width of the envelope exhibits a positive relationship with increasing damage depth, and was therefore determined to be appropriate both for estimating the damage width, and the damage depth.A) is shown in The area of the envelope , and therefore no intuitive pattern for the height and width of the peak due to the damage width was identified. However, for a damage depth of 1 mm, the width of the peak increased gradually from 1 mm to 6 mm with increasing damage width. However, when the damage width was extended from 6 mm to 9 mm, the peak was distorted and the height of the peak decreased. Additionally, the width of the peak did not significantly increase either, and unlike damage depth, there was no consistent pattern, thus confirming that the characteristics vary slightly depending on the size of the peak.The change in leakage flux signal with increases in the damage width was investigated by quantifying the damage index determined using various characteristics of the peak.W), commonly known as a representative index that reflects the width of the damage, was extracted and is shown in First, the P-P width (P-PW) as a function of the damage width variation.The index decreased at a depth of 0.5 mm without increasing stepwise at a width of 6 mm. This is because the peak size itself is too small, as can be seen by comparing with the peaks in To compensate for the effects of size reduction, the FWHM extracted at the half height of the peak was used instead, as shown in Extracted FWHM values exhibited a positive correlation with increasing width, similar to the P-P value. However, the values for the 1 mm width index and 3 mm width index were still not completely separated. However, since it was confirmed that changes in the index follow a certain pattern, this index can be effectively used when constructing the pattern recognition algorithm.To estimate the depth and width of damage in the wire ropes, an ANN based pattern recognition technique was applied in this study. Estimation of the damage size was performed using a two-step ANN pattern recognition process as shown in Various damage indexes were used for training the ANN classifier for damage depth estimation. Based on the previous section, it was confirmed that the P-P value, peak value of the envelope, area of the envelope, and width of the envelope have positive relationships with increasing damage depth. Therefore, the ANN classifier was trained using all four kinds of damage indexes.The distribution of the learning data obtained by mapping it into three-dimensional space is displayed in As shown in As mentioned above, the ANN classifier was trained using four kinds of damage indexes, each extracted from 40 data points according to the damage depth.To verify the performance of the ANN classifier, the damage indexes were extracted in the same way using the 40 test data collected under the same conditions, which were then substituted into the learned ANN classifier to estimate the depth of the damage. Estimation of the damage using the ANN is shown in Next, an algorithm for estimating the width of the damage via pattern recognition techniques using the ANN as well as damage depth estimation was investigated. The learning method for the ANN is similar to the method of damage depth estimation. Previously, the depth and width of the damage already confirmed that the damage indexes are sensitive to each other. Thus, the damage indexes used for depth estimation as well as other combinations of damage indexes were used for ANN learning for width estimation.The FWHM and P-P width showed clear increasing patterns as the damage width increased. Therefore, this combination is a useful index for ANN classifier learning for the estimation of damage width.In addition, for the MFL signal, it was already confirmed that the depth of damage can also affect the damage index in terms of the damage width. Therefore, the ‘estimated depth value’, which was previously constructed during the damage depth estimation step using the ANN, was used as an auxiliary index for considering the damage depth. Therefore, P-P width, FWHM, and estimated depth value were used as the ANN learning data for width estimation. These are presented in three-dimensional space in As shown above, 40 sets of learning data were used for each damage width, and the ANN classifier was trained for width estimation.Subsequently, in order to verify the performance of the ANN classifier with regard to width estimation, 40 data per damage width were collected under the same experimental conditions as the training data used to the classifier. The results of the damage width estimation by the ANN classifier are shown in All 40 of the estimates for damage width closely matched the damage width of the actual wire rope. There was an error of less than 0.1 between the measurement intervals, as can be confirmed by Therefore, it is expected that effective quantitative wire rope inspection can be provided through application of the MFL based NDE technique and the ANN pattern recognition technique presented in this research.(1)Magnetic flux leakage was detected at locations with actual damage by using a Hall sensor located near the damage.(2)The MFL signals at the damaged areas became more apparent via the enveloping process based on the Hilbert transform.(3)Envelopes of the MFL signal exceeded the thresholds based on the GEV distribution around areas with actual damage.(4)Damage indexes were extracted to quantify the MFL signals; these damage indexes can classify the damage size according to increases in damage size.(5)Four types of damage indexes based on the relationship between the envelope signal and the threshold were proposed. These damage indexes can improve the accuracy of quantification of the damage size.(6)Two-step ANN based pattern recognition was applied to estimate the depth and width of the damage. The ANN classifier was trained using multi-dimensional damage indexes extracted from the MFL signals; the trained ANN classifier can successfully estimate the size of damage with little error.The MFL-based NDT technique was used to detect damage in steel wire ropes. An MFL sensor head was fabricated and a series of experimental studies were performed to verify the feasibility of the proposed technique. In addition, damage indexes were extracted to quantify the size of the damage. An ANN-based pattern recognition method using the extracted damage indexes was used to automatically estimate the amount of damage. This approach to wire rope NDE was confirmed through the following observations:Overall, these results demonstrated that the proposed damage detection and quantification method using MFL sensors and an ANN classifier is capable of diagnosing defects in steel wire ropes. This approach will be complemented and validated by further research performed on various types of damage and environments.In addition, it is expected that the proposed wire rope NDE method can be utilized as an advanced inspection tool for real-time in situ wire rope monitoring in combination with the Internet of Things and robot technologies.


A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit–receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit–receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit–receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods. Multiple-input multiple-output (MIMO) technology has become an active research topic during the last decade due to its capability for achieving the high transmission rates required by an increasing number of data-demanding applications. MIMO technology provides a plenty of benefits that allow dealing with the challenges posed by the impairments in the wireless channel, especially multipath fading . It provAn accurate MIMO channel model is important to exploit the advantages provided by MIMO systems. This should be done in a realistic way that accurately describes the propagation channel. Recently, the issue of sparsity of the wireless channel has received a lot of attention. It is a fact that, in many scenarios, wireless channels can be characterized by few significant paths due to a few dominant scatterers distributed in the environment, causing a sparse channel impulse response (CIR) ,7,8,9,10In this paper, a new derivation of the channel model based on higher-order Taylor expansion is proposed. It is worth noting that some previous derivations of the channel model with scattering have been proposed in other contexts  but were limited to the first-order Taylor expansion ,19,20. Anth transmit antenna and mth receive antenna can be modeled as:L is the number of propagation paths (clusters), with l, respectively, between the nth transmit antenna and mth receive antenna, where l shared by all the transmit–receive antenna pairs, and pth contributing ray of path l between the nth transmit antenna and mth receive antenna.For an ented in ,19,20 annth transmit antenna with a constant rate L paths. For the mth receive antenna, the received baseband signal is:In the considered For notational convenience, the additive noise is not represented in the above equation.K is the considered number of Fourier coefficients and K is an even integer.Applying the discrete Fourier transform (DFT), the Fourier coefficients of the received signal are given by:Let Equation  can be rAs the deviations Equation  gives: with adU is the order of the Taylor expansion that should be estimated to define the effective dimension of the signal subspace. This can be done through analyzing the eigenvalues of the estimated covariance matrix. The MDL and the Akaike information criterion (AIC) [L from the value provided by the MDL criterion. In fact, for L paths is assumed to occupy the same degrees of freedom U according to the following rule:From the generalized model in , the dimon (AIC)  are the on (AIC) . Such meon (AIC) . The MDLon (AIC) . The AICn,m)) in  is assumU changes according to the SNR as well as the standard deviation of the delay spreading. It follows that the eigenvectors associated with the The covariance matrix of ed as in . As showFor the case of no delay spreading, the conventional MUSIC can be applied to estimate the delays where the vector Equation , vectorsThe derived cost function can be written in the following form:The proposed cost function turns to be the conventional MUSIC when setting Algorithm of the proposed methodL is assumed to be known. Collect the matrix Y .Estimate the covariance matrix RY .Apply the eigenvalue decomposition on Estimate the effective dimension of the signal subspace using the MDL criterion combined with the proposed rule .Construct matrix t, calculate the cost function L peaks of For each value of tion P(t). Then, tNumber of delays The algorithm used for the estimation is summarized as follows:The proposed cost function shows no major difference from the conventional MUSIC in terms of the computational complexity as they share the same operations such as the FFT, l for each ith experiment and Simulations are carried out for a U and MUSIC.U to benefit from a better approximation, where small details can be taken into account. On the other hand, with noise-contaminated data, the highest order terms in Taylor expansion  may be Equation  and 6),,U to benHence, for U versus different intervals chosen for the delay spreading, for SNR = 5 dB.U.From We can also observe that as the delay spreading increases, the estimation is less accurate, which is probably due to the approximation error of Taylor expansion.U, the MDL criterion is applied on the eigenvalues of To determine the most appropriate value of Observing However, it can be noted that the different flat regions are not always clearly distinguished. For example, when the delay spreading is within U is obtained from the rule in Equation  is applU and provides the best mean path delay estimation. However some minor failures of the MDL criterion in systematically estimating the optimal effective dimension of the signal subspace can be noted.As shown in The obtained results show that the MDL criterion provides better estimations of the different effective dimensions of the signal subspace for different delay spreading intervals, and the different regions are well distinguished, leading to an improvement in the mean delay estimation performance of the proposed subspace tracking-based method. As mentioned before, the increase in the RMSE with respect to the delay spreading may be due to the approximation error in Taylor expansion. However, as shown in The obtained results show a moderate improvement in the estimation performance as SNR increases. In fact as SNR increases, better estimation of covariance matrix is obtained, and hence there is better estimation of signal or noise subspaces.As one path delay (A parametric subspace-based estimation method is proposed in this paper to deal with a non-strictly exact common support MIMO channel model that exhibits delay spreading due to scattering. A parametric model of the MIMO channel is first derived. Then, a subspace-based method is developed based on the tracking of the effective dimension of the signal subspace, which depends on the channel features. The proposed subspace tracking-based method is applied to estimate the channel mean delays. The proposed method shows better performance in comparison to the conventional methods, and allows estimating channel mean path delays with more accuracy, leading to a better MIMO channel path delay estimation in scattering environments.