Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters ) and physicochemical parameters (e.g., chlorophyll content ( Vegetation modelling is a nondestructive technique for quantifying vegetation properties and analysing the physiological conditions of crops, such as growth potential and nutrition . The soiRemote sensing techniques are rapidly developing. These techniques allow time-effective and noninvasive data collection over large scales ,12,13,14m2 soil) , chlorop μg/cm2) , and veg weight)  are impo weight)  confirme weight) . Tan et  weight)  found th weight) . Previou weight) . Previou weight)  found th weight) ,25 highl weight) . The mea weight) . The effntration . Owing tntration , the relntration , is simpntration  emphasizntration ,32. FASTThe purpose of this study is to explore the method of simulating the winter wheat spectrum with the mSCOPE model and verifying its rationality from multiple perspectives. Firstly, the appropriate number of layers was selected. Based on the spectral data generated by mSCOPE simulation, the correlation between vegetation index and spectrum was analysed. Then, the accuracy of vegetation index (VIs) inversion was evaluated using statistical indicators. Finally, SA was carried out to further verify the rationality of model operation and parameter selection. The rest of this study was organized as follows. The winter wheat data of Xiaotangshan National Precision Agriculture Research Demonstration Base in 2015 were combined with the biochemical parameters of the PROSPECT model as a reference to identify a reasonable parameter range as inputs . AccordiThe mSCOPE model was used to obtain spectral data. Vertical stratification was performed on the basis of the SCOPE model. The modules of the mSCOPE model still followed the SCOPE model and mainly included four modules: one biochemical model (Fluspect) and three energy balance routines . The Fluspect module simulates the reflectivity, transmittance, and fluorescence of a blade . RTMo anThe mSCOPE model stratifies the canopy in accordance with the principle of uniformity. The height of each layer is 1/N of the total height from the soil to the top of the canopy. In order to simplify the calculation and facilitate the implementation of the algorithm, the average height hierarchical way was carried out. The mSCOPE model integrated the vertical changes of vegetation attributes, but it did not consider the horizontal changes. Therefore, mSCOPE can be regarded as a 2D model . The mSCThis study referred to several spectral indexes proposed by predecessors to make a further analysis between the input parameters of mSCOPE model and the spectrum to study the correlation between them. These spectral VIs were mainly divided into two categories: two-band spectral index and three-band spectral index . Due to The canopy was divided into 1 up to 60 layers in the mSCOPE model. To select the optimal number of layers, we conducted seven experiments, in which LAI and chlorophyll content were selected as the controlled variables. Because water content mainly affects the near-infrared band and has little influence on the vegetation index selected to evaluate the results, it can be ignored in this scenario. The experimental scenario was set as shown in In order to distinguish the simulation scene from that of the simulation work, the latest winter wheat data of Xiaotangshan National Precision Agriculture Research and Demonstration Base in April 2020 was selected to verify the inversion accuracy of the model. The parameters in The simulated spectrum was obtained and then the corresponding VIs were calculated. In order to analyse the accuracy of simulation results of mSCOPE model, we compared simulated VIs values with real VIs values of 21 groups through deviation (Bias) and root mean square error (RMSE) analysis. The formula for calculating deviations between measured and simulated values is as follows:y and N indicates the total experiment numbers; and i represents the i-th experiment. In the experiment, 21 different scenarios from dense to sparse without removing ear of winter wheat in booting stage were selected for simulation  which was the running time when output the layered spectrum, was measured in seconds. The error (A) was obtained from the absolute difference between the simulated vegetation index and the mean value. Efficiency is inversely proportional to time (T) and error (A). The statistics of running time error and efficiency are shown in The program run time  during photosynthesis of crops . Comparencreased a. As chlncreased b. The inncreased c. The reThe relationship between vegetation index and biochemical properties of vegetation in specific spectral combinations of bands is also of great interest to us. Firstly, correlation analysis between VIs and LAI was carried out. Since the sensitive bands and robust vegetation index proved in previous experiments were selected, the results obtained were satisfactory. The correlation coefficients of TOC LAI with NDVI and RVI were 0.9687 and 0.9925, respectively, by which the selected VIs showed a good positive correlation between LAI . The ratFirst of all, the real parameters were inputted into the mSCOPE model to obtain the simulated spectrum and the simulated value of VIs. The measured values of VIs were calculated using the measured spectrum, and deviations were calculated together with the simulated value of VIs. We ran 21 sets of real data and calculated the RMSE for each VIs and its maximum and minimum values .The results showed that RMSE of Due to the limited manpower and material resources, it is difficult to obtain and calibrate all the parameters involved in the model. To reduce the number of parameters, SA should be carried out according to local crop varieties and its growth conditions. On the one hand, SA should be performed before a model is used because the extraction of major parameters improves the accuracy and efficiency of simulation work; On the other hand, because the physiological characteristics of different crops (such as LAI and plant height) are generally different, the sensitivity of corresponding parameters should be analysed again to judge if the parameters need to be localized and regionalized.As can be seen in Differences in the optical properties of crop canopies were rarely considered. The relationship between vegetation biochemical contents stratified within the canopy and spectral reflectance characteristics could be easily investigated by changing several parameters in the model. However, this step was difficult to realize in actual field experiments. Results indicated that the spectral characteristics changed, and the correlation between the spectrum and chlorophyll content was demonstrated well. The input data of the mSCOPE model could be improved by simplifying its complexity; some model parameters were complicated and difficult to obtain. The initialization values that come with the model may lead to inevitable system errors. Moreover, the inversion process involving inputting different vertical layer parameters was uncertain. The same spectrum may be obtained by adjusting the input variables, and the underdetermined results in practical applications must be judged on the basis of experience to a certain extent. Nevertheless, the mechanism and principle of the mSCOPE model were easy to understand, and its implementation is clear. It had good explanatory ability in simulating photosynthesis and light transmission within the canopy. Moreover, it extended the longitudinal scale of the canopy, and the simulation results it obtains are reasonable, efficient, and accurate. The way the model works can be extended and applied to other homogeneous canopy models. Nitrogen and chlorophyll are closely related. We simply verified that winter wheat with different nitrogen content had a good performance, indicating that this model had a broad application prospect. This conclusion can also be extended to other fields where practical scenarios are combined. For example, diseases and insect pests may cause yellower leaves in the middle layer. Common spectral observation method generally focuses on the top of the canopy. However, this way may cause trouble. When the top layer of leaves can detect spectral changes deterioration, the disease has intensified. Using mSCOPE model to invert physiological indexes such as VIs of canopy interior stratification is undoubtedly a good way to solve the difficulty of internal spectral measurement.The mSCOPE model was used to simulate the spectrum by inputting easily obtained vegetation parameters. The indexes that could not be readily measured were obtained via simple calculation and inversion. In terms of deviations, the simulation effects of the model on NDVI were considerably better than those of RVI. However, the difference in RMSE between the two was not significant. This conclusion requires further verification with more experimental data. Unlike the unstratified inversion accuracy, the measured values of the top layers were used as input parameters of the single-layer canopy. Results showed that its RMSE was greater than 10%, which was substantially higher than stratification accuracy. In the subsequent data collection, as many data as possible required by the model should be collected to eliminate systematic errors and maintain simulation conditions as close as possible to measurement conditions. For parameters that are difficult to obtain, the inversion accuracy and efficiency can be improved by setting a reasonable value range combined with prior knowledge. In general, the fitness of inversion was relatively good, and the simulated value had certain reference value. The best solution to avoid systematic errors is to obtain actual field data. In doing so, model accuracy can be clearly debugged and make adequate preparation for practical applications. The function of this model, which can simultaneously output reflectance and fluorescence spectra, is very powerful. However, this model has many features that must be improved. For example, outputs and inputs are manually performed. A visual interface can be added to save time in running the model and improve its efficiency.The influence of a single-layered variable on the reflectivity of different bands was analysed via the FAST algorithm. Global sensitivity was also obtained. The coupling effects of input parameters were also considered. Given that the physiological characteristics of winter wheat are systematic responses to physicochemical contents, a single aspect is insufficient to explain its biological characteristics. The results also demonstrated that the FAST method can simultaneously analyses the direct and indirect effects and the local and global coupling effects of each parameter. However, the present study provided a simple verification only. Owing to space limitation, we did not compare this method with other sensitivity analysis methods. A follow-up study may be conducted to include this aspect. Nevertheless, the present study has a certain reference value. This method has broad application prospects and may be applicable not only to the mSCOPE model developed herein but also to any other model.We outputted the layered reflectance spectra and discussed the advantages and generalization ability of the improved mSCOPE model based on the SCOPE model. Sensitivity indexes of the main parameters were calculated via sensitivity analyses of 585 experiments. Results confirmed that weight ratio was directly related to the advantages and disadvantages of the sensitive factors in spectral reflectance. The effects of vertical distribution of leaf physiological characteristics on reflectance spectra were considered. Scenario simulation analysis revealed that the model was optimal in the case of three layers. Moreover, the vertical distribution patterns of LAI,


Interesting correlations between several kinematic parameters and muscular mass were highlighted in the fatigued group. Finally, a “fatigue approximation index” was proposed and validated as fatigue quantifier.Quantifying muscle fatigue is a key aspect of everyday sport practice. A reliable and objective solution that can fulfil this task would be deeply important for two main reasons: (i) it would grant an objective indicator to adjust the daily training load for each player and (ii) it would provide an innovative tool to reduce the risk of fatigue-related injuries. Available solutions for objectively quantifying the fatigue level of fatigue can be invasive for the athlete; they could alter the performance or they are not compatible with daily practice on the playground. Building on previous findings that identified fatigue-related parameters in the kinematic of the counter-movement jump (CMJ), this study evaluates the physical response to a fatigue protocol  in 16 football referees, by monitoring CMJ performance with wearable magneto-inertial measurement units (MIMU). Nineteen kinematic parameters were selected as suitable indicators for fatigue detection. The analysis of their variations allowed us to distinguish two opposites but coherent responses to the fatigue protocol. Indeed, eight out of sixteen athletes showed reduced performance , while the other eight athletes experienced an improvement of the execution likely due to the so-called Post-Activation Potentiation. In both cases, the above parameters were significantly influenced by the fatigue protocol ( Muscle response after a preload stimulus is the result of its adaptability. Indeed, muscles are characterized by an intrinsic plasticity that allows them to conveniently calibrate their force, endurance and contractile speed according to the effort they are subjected to. This phenomenon is particularly evident in sport, where the specific adaptation of the muscle tissue after consecutive training sessions allows the athlete’s performance to be maximized   and theSeveral methods were used to quantify training load, such as questionnaires, diaries, physiological monitoring and direct observation . QuestioPrevious works suggested the possibility to identify fatigue markers in a non-invasive way by studying the athlete’s performance in counter-movement jump (CMJ), which is a well-established practice in sports providing information on both the fatigue in the athlete and his supercompensation due to a periodic training path ,14,15,16Specifically, the aim of this work is to study variations in human kinematic parameters after a football-specific fatigue protocol  using wearable sensors and to analyse these variations so as to detect and quantify the fatigue condition in athletes. In other terms, the main hypothesis of the present study is that fatigue could alter kinematic parameters in semi-professional athletes during the execution of a simple gesture, such as the Counter-Movement Jump, and that wearables can be used to track their variation. In order to achieve this objective, a body sensor network based on a magneto-inertial measurement unit (MIMU) was used. MIMUs and wearables in general, in our view, are the key enabling technologies for an in-field fatigue assessment, which is extremely important in the daily practice of athletes and coaches. MIMUs are very versatile, cheap and small in size sensors. They can be used in uncontrolled environments too (enabling the possibility to test methods directly on the playground), but they are characterized by a minor accuracy compared to optoelectronic methods. For this reason, they require particular care with regard to computational strategies to extract reliable information from them. Nevertheless, previous studies validated MIMUs for jump kinematic analysis, proving that there is no statistical differences between the accuracy of these units and that of an optoelectronic system in parameters extracted during this specific gesture .Sixteen semi-professional male football referees  were involved in testing. Athletes, in accordance with the Helsinki protocol, signed an informed consent form and filled out a questionnaire to take part in the tests. No subject had a record of cardiovascular/respiratory disease or evidence of neuromuscular diseases in the twelve months prior this study. This study was approved by the Joint Ethics Committee of the Scuola Normale Superiore and Scuola Superiore Sant’Anna .A 5 min warm-up phase of jogging on treadmill at fixed speed ;Pre-fatigue acquisition, in which MIMUs were placed on the participant’s body using elastic bands, and the athlete performed one series of 5 CMJs separated by a 10 s of rest time (at the end of this acquisition sensors were removed to facilitate the following phase);Fatigue phase induced in athletes by using the YYIR1  was repeated after 5 min from the end of fatigue exercises.The acquisition protocol was conducted in a controlled environment , and it consisted of the following steps:Pre- and post-fatigue parameters were estimated from the most similar four jumps in terms of body centre of mass acceleration traces  and then averaged of the relative series. The pre-fatigue acquisitions were used as a baseline to compare data from post-fatigue measurements. Data were acquired using a body sensor network of four MIMUs  laterally placed on the right foot, right thigh, right leg and pelvis . The fooIn order to estimate the correct acceleration of the body centre of mass, a preliminary alignment of the sensor with the gravity vector was performed by using the quaternions provided by the inertial unit located on the pelvis. After this initial alignment, gravity was subtracted from the acceleration of the body centre of mass to obtain its inertial acceleration. All the above parameters were estimated starting from this inertial acceleration and neglecting the influence of the gravity. This procedure allowed us to isolate only the contribution of the countermovement to the COM kinematic parameters. The typical trace of the body centre of mass inertial acceleration in the vertical direction is shown in The start of the eccentric phase was identified by analysing the acceleration curve and automatically finding when it goes below a specific threshold ;The end of the eccentric phase corresponds to the zero crossing of the velocity  is analysed in this work. In our opinion, monitoring the mean position of the barycentre could be useful to understand how much the athlete is loading the jump due to fatigue.Power and energy parameters: allow us to quantify the lower-limb explosive ability, resulting in extremely important indicators for fatigue detection. An in-depth analysis of the scientific literature allowed us to find the following parameters [rameters ,17,24: mVelocity parameters [rameters ,24: usefTime parameters [rameters ,24: alloIn turn, the COM kinematic parameters could be divided into four main groups:Concerning articular joint kinematic parameters, since the main muscles involved in vertical jumping are hip and knee flexors/extensors, the knee and hip mean flex-extension angular velocities were considered. Similarly to velocity parameters, joint angular velocities could quantify the effort encountered by the athlete during the exercise. In addition, temporal symmetry index (TSI) and duration of the eccentric phase as a percentage of the counter-movement total duration (Eccentric %) were included to monitor how the temporal symmetry of the gesture (eccentric vs. concentric phase) is influenced by fatigue. Although the abovementioned studies estimated the parameters for all the phases of the jump (eccentric + concentric) ,16, thisAs previously mentioned, two categories of parameters were considered: COM kinematic, which refers to the centre of mass of the athlete’s body, and articular joint kinematic .COM kinematic parameters were estimated by processing on “Matlab” , the measurement of acceleration of the MIMU placed in correspondence of the fifth lumbar vertebra using formulas reported in A preliminary comparison of modified power parameters before and after fatigue induction allowed us to understand if the subject was either in the fatigue or in the PAP group: participants who showed a reduction in the relative power exerted (symptom of a decreased performance) were grouped as fatigued, while those that showed an increase in the power exerted  were grouped as potentiated . Such grectively . In otheAfter this initial partition, normalized differences . For all the tested parameters in both groups, the Kolmogorov–Smirnov test rejected the null hypothesis that they came from a standard normal distribution for all the sixteen participants. The effects of the YYIR1 test on the two groups  is reported separately in the text below.p < 0.05). According to Cliff’s Delta test, a heavy exercise had generally medium effects (M) on kinematic parameters. Parameter variations are reported in Eight out of sixteen athletes showed a reduction in the jump performance. In terms of parameter variations, the execution of a heavy conditioning exercise caused a reduction in counter-movement velocity, exerted power, released energy and articular joint velocities and an increase in execution and contact times  as a resp < 0.05), validating them as indicative factors for performance monitoring, while Cliff’s Delta test revealed generally large effects (L) on kinematic parameters. A mean absolute normalized variation of 34.4% was registered. No significant correlations were observed between anthropometric parameters and kinematic parameters.The remaining eight participants were characterized by a potentiation. The increase in counter-movement velocity, exerted power, released energy and articular joint velocities and the decrease in execution and contact times were registered . TherefoThe comparison of the values of nineteen kinematic parameters before and after a football-specific fatigue protocol  confirmed our hypothesis. The Wilcoxon signed rank verified that all the parameters were significantly influenced by the execution of heavy exercise, suggesting that they can be used in a fatigue monitoring solution based on wearable sensors. As mentioned in the Introduction section, two opposites but coherent responses to the fatigue protocol were distinguished. Such differentiation is in accordance with the scientific literature, in which the muscular response is modelled as the contrast between fatigue and potentiation . TherefoConversely, athletes who underwent a potentiation showed the opposite behaviour: increased exerted power, velocity and released energy and decreased executions times. Since higher order muscle fibres are involved in the potentiation, in this case, athletes are faster and the counter-movement gesture is more symmetric (decreased TSI and eccentric duration), while the eccentric phase duration is reduced due to a better elastic force exploitation. In contrast to the first group, in this case, no significant correlations were found between physiological/anatomical parameters and COM kinematic/articular joint kinematic parameters.Regarding fatigue quantification, the FAI definition was based on the logic presumption that since the exerted power is an indicator of the athlete’s lower-body explosive ability, it could be reasonably considered as the most sensitive parameter to fatigue . Thus, FThis study shows some limitations. Indeed, the participant sample was characterized by a high variability in terms of age and year of agonistics practice, resulting in a heterogeneous study population. In particular, since participants belonged to different training teams, different referees’ preparation could introduce additional variability in the observed data. In addition, the YYIR1 was based on the participants’ subjective perception of fatigue and no physiological parameters were measured to objectively verify the effectivity of the fatiguing exercise. In addition, since the quantification of fatigue is based on external sensors , the evaluation of fatigue is indirect. The use of sEMG as a wearable alternative could have given useful information on the lower-limb muscles activation and their ability to exert force in association to fatigue. This information could have been compared with the FAI to validate it as an objective index for quantifying fatigue. However, the assessment of fatigue from muscle electrophysiology by means of EMG was impractical, due to the significant sweating of athletes during the proposed test that would result in signal artifacts . Neverth


The Savitzky–Golay filter (SGF) is a time-domain technique that determines a trend line for a signal. The direct application of SGF for damage localization and quantification is investigated in this paper. Therefore, a single-stage trend line-based damage detection method employing SGF is proposed in which the damage is located and quantified at the bridge under moving load. A simply supported beam under moving sprung mass is numerically simulated to verify the proposed method. Four different velocities and five different single- and multi-damage scenarios are considered. The acceleration data along the beam are obtained, manually polluted with noise and their trend lines are then determined using SGF. The results show that the proposed method can accurately locate and quantify the damage using these trend lines. It is proved that the proposed method is insensitive to the noise and velocity variation in which having a constant velocity is a hard task before and after damage. Additionally, defining a normalization factor and fitting a Gaussian curve to this factor provide an estimation for the baseline and therefore, it categorizes the proposed method as baseline-free method. Health monitoring of bridge type structures subjected to a moving load is a hot topic that has received much attention in the last few decades ,2,3,4. AThe effectiveness of transformers, such as the wavelet transformer, to locate the damage from bridge vibration data  was discussed by many researchers ,7,8,9. HOne of the famous example of time-domain techniques is random decrement technique (RDT), which aims to average the transient response of the signal out . Lee et Source separation approach is an output-only technique that determines the input excitations from output responses. It employs different techniques such as second order blind identification (SOBI) in which the modal parameters of structure are the side results of this approach. Loh et al.  used SOBBased on the authors’ knowledge, there is a lack of investigation on the direct use of time-domain filters in the field of structural health monitoring (SHM). Moving average filter (MAF) is one of the famous time-domain filters that was frequently highlighted as a noise reduction filter ,23,24. DThe above literature mostly uses modal parameters such as the damage index (DI) which only locates the damage in bridge type structure. Moreover, their backgrounds are complex that make them time-consuming and costly. The authors previously proposed a double-staged BHM method using a combination of RDT and SGF . Since t…,…,r and This section describes the procedure of determining a trend line for a signal. Employing the least-square fit and a polynomial function as a filter kernel, SGF is able to reduce noises and find a trend line for a signal ,27,32,33SGF uses all the Employing the SGF, an output-only baseline-free time-domain bridge damage detection method is developed in which the bridge acceleration response is used. As shown in The numerical investigation of simply supported beam under truck load was addressed by many researchers ,5,25,31.Finite Element Model (FEM) of a simply supported bridge under moving sprung mass was established in which the bridge and vehicle were considered with the detail shown in As listed in An example of acceleration data obtained from node 5 (middle of the bridge) is shown in As mentioned in E is the energy of the signal. Obviously, the amplitude of the trend lines determined from different nodes of the bridge are different and need to be normalized as follow:i and i and The insensitivity of DIs based on the modal parameters such as natural frequency were addressed by many researchers ,40,41. CAs shown in As mentioned above, it is clear that the damage cannot be quantified in higher velocities since it cannot be located with high accuracy. Due to the lack of access to the experimental result, the proposed method was numerically proved. However, it is proved that temperature variation, road profile and other environmental phenomena may have effect on the results. Therefore, a signal of noise using white noise was randomly produced and manually added to the acceleration signals. To calculate the amount of noise, the root mean square (RMS) ratios of the noise to the signal calculated for all signals. The RMS ratios of the noise to the signal for each node for velocity 1.25 m/s are up to As shown in As illustrated above, The DIs calculated using Equation  successfThis section discusses the effect SGF with different order and span on the results. The changes in natural frequencies is also reported here. All the cases studied here are under moving sprung mass with velocity 1.25 m/s.To optimize the accuracy of the proposed method, the span of SGF is separately determined for each scenario. This section discusses the effect of selecting a unique span for all cases. Therefore, no matter what is the natural frequency of the bridge, SGF with unique span is applied to all cases. As shown in This subsection provides the results of proposed method using SGF with different orders. The environmental conditions such as temperature variation can change the natural frequencies. The interaction between vehicle and bridge also causes shift in the natural frequencies of both bridge and vehicle. Moreover, it increases the maximum vertical deflection of the bridge as well. Many factors such as weight, velocity, springs, and damper of the vehicle can increase the vertical deflection of the bridge. There are some literature addressed this effect ,44,45. SThe effect of interaction between bridge and vehicle is illustrated here using two terms namely Dynamic Amplification Factor (DAF) and frequency response of the beam. DAF is defined as below:There are various damage detection methods declare that the natural frequency should be changed at least 5% to have an accurate detection of damage. Some of them addressed that even temperature variation sometimes changes the natural frequency more than 5% ,42. It iDamage decreases the natural frequencies and makes the bridge softer. The main objective of this paper was to locate and quantify the damage in the bridge structure. Therefore, this paper investigated the direct use of SGF to develop a single-stage trend line-based damage detection method for a BHM system. The accuracy of the proposed method was numerically verified using a simply supported beam under moving sprung mass with different velocities. The acceleration data at different places along the beam were recorded, manually polluted with noise, and their trend lines were determined using adjusted SGF. Using normalized energy-based DIs calculated from these trend lines accurately located and quantified the damage. Therefore, it is proved that the trend line of an acceleration signal has signature of damage, so it can be used for damage detection purpose. Additionally, the results prove that the order=3 and span base on the first natural frequency can optimize the accuracy of the proposed method. Therefore, the objective of this study is fully achieved.Since the SGF is a de-noising technique, the proposed method is essentially insensitive to the noise.The proposed method could locate/quantify the damage in noisy/noise-free environment.Fitting a Gaussian curve to the normalization factor makes the proposed method as a baseline-free method.The proposed method can locate the damage in a multi-damage scenario.The main advantages of the proposed method is as follow:The last but not least, the proposed method does not need neither prior knowledge of damage nor knowledge of input data. Therefore, the proposed method is categorized as output-only damage detection method. This feature makes the proposed method very suitable for practical cases. It should also be mentioned that the main practical limitation for this method is the velocity. Increasing the velocity decreases the accuracy of the proposed method.


In recent years, we have seen significant interest in the use of permanently deployed resident robotic vehicles for commercial inspection, maintenance and repair (IMR) activities. This paper presents a concept and demonstration, through offshore trials, of a low-cost, low-maintenance, navigational marker that can eliminate drift in vehicle INS solution when the vehicle is close to the IMR target. The subsea localisation marker system is fixed on location on the resident field asset and is used in on-vehicle machine vision algorithms for pose estimation and facilitation of a high-resolution world coordinate frame registration with a high refresh rate. This paper presents evaluation of the system during trials in the North Atlantic Ocean during January 2019. System performances and propagation of position error is inspected and estimated, and the effect of intermittent visual based position update to Kalman filter and onboard INS solution is discussed. The paper presents experimental results of the commercial state-of-the-art inertial navigation system operating in the pure inertial mode for comparison. In recent years, we have seen the conceptual introduction of resident robotic platforms to underwater inspection, maintenance and repair (IMR) activities within the oil and gas sector. This is being driven by cost saving activities to reduce the levelized cost of energy (LCOE) for the sector and in an effort to compete with emerging sectors such as hydraulic fracturing. Traditionally, subsea IMR activities are carried out using a work-class hydraulic remotely operated vehicle (ROV). Vehicles are tethered and controlled directly from a surface support vessel and generally incur significant expenditures associated with the cost of the vessel and crew. The concept of permanently deployed ROV systems has been in the literature for many years ; howeverThis extended weather capabilities has hugely significant benefits, not just to oil and gas but also to the offshore wind sector, which has seen the rollout of many large infrastructure projects off European waters in recent years. However, typically within offshore wind and oil and gas, multiple assets can be spread across hundreds of square kilometres of seabed, and a resident vehicle capable of performing an inspection across these multiple distributed assets would be required. This mobility is being addressed through developments in resident autonomous underwater vehicles (AUVs) ,8. HowevTraditionally, underwater navigation systems are based on acoustic systems, providing an absolute world coordinate frame position solution with technologies such as Long baseline (LBL) or Ultra-short baseline (USBL) systems . HoweverVarious research has been completed in the field of navigation sensor fusion ,12, withThe fact that resident vehicles will operate in partially structured environments consisting of known subsea structures  can be utilised and can offer a means of facilitating a low cost, low maintenance, navigational marker that can eliminate drift in INS solution when the vehicle is close to the IMR target. This research paper discusses the use of an LED based subsea location marker at a known structure position for vehicle position update. The localisation aid provides pose estimation based from active light marker beacons for intermittent resident ROV position update and intermittent drift elimination close to target, where accuracy is required. This paper presents evaluation of the system during trials in the North Atlantic Ocean during January 2019. System performances and propagation of position error are inspected and estimated, and the effect of intermittent visual based position update is discussed.The experimental setup, for the trials performed during January 2019 in North Atlantic Ocean, is shown in The system for visual pose estimation consists of a machine vision camera IDS uEye with Sony 1/1.8” CMOS (IMX265), and with fixed focus Lensagon BM4518S lens. The camera is enclosed in subsea housing with flat port. Four conventional subsea LED lights Bowtech LED-K-3200-DC were used as a light marker. The lights, standard throughout the sector, are shown in The heart of the ROV navigation system is the state-of-the-art inertial navigational system PHINS 6000. The system is coupled with a DVL Nortek 500, a depth sensor, an ultra-short baseline system Teledyne Ranger 2, and a differential GPS unit Okeanus GPSR-3015G. The technical specifications of the navigational system components are given in Inertial measurement unit (IMU) which consists of three fibre optic gyroscopes and three accelerometers,Inertial navigation system (INS) resolving inertial measurements and updating position, velocity, and attitude,Extended Kalman filter for optimal integration of external and internal sensor measurements.Commercial inertial navigation system PHINS 6000 was used during the trials. The system presents a gold standard within marine robotics navigation and consists of three major components:The navigation system PHINS 6000 operates with various external sensors such as GPS, USBL/LBL, depth sensor, and DVL. The system operates in various modes depending on the combination of external sensors used. The GPS is usually used for initial heading alignment process while on the ship’s deck, and to acquire an absolute, geo-referenced position while ROV is on the surface.INS + USBL/LBL + (DVL),INS + DVL,INS .While operating underwater, there are three PHINS operating modes based on the external sensors used:The best pose estimation performance is achieved while PHINS operates in USBL + INS mode. The USBL provides an absolute position while INS reduces the noise on the USBL position measurements. As shown in Without USBL/LBL absolute position fix, the position measurement drifts over time. The DVL + INS operating mode minimises the drift due to a constant speed over ground measurement. In this mode, the position drift of PHINS 6000 is approximately 0.1% of the travelled distance. Pure inertial mode without external sensor aiding is considered a worst-case scenario. The position error is estimated based on the integration of the IMU data, thus it depends on the quality of the FOG and accelerometers.Position estimation accuracy in the pure inertial mode depends on two factors: (1) the accuracy of the IMU acquired measurements, therefore the accuracy of the accelerometers and FOGs, and (2) an initial position, velocity, and attitude errors obtained after the alignment/position fix. Considering the IMU data are integrated over time, the position error contained within the INS covariance error matrix propagates with time as well. Therefore, assuming the same initial position error in the pure inertial mode, the propagation of the position error is only a function of time. In practice, the initial position error varies mostly due to the quality of the position fix. During few days of the ROV operations, the position error based on USBL position fix was between 0.23 m and 0.57 m. Some of the factors that may influence the quality of the position fix are the strength of the USBL signal, a partial USBL responder occlusion, subsea noise pollution, and distance between transponder and receiver.The propagation of position error in the pure inertial mode was further studied. The continuous line shows recorded position error propagation over time. During the tests, the ROV was manually piloted while performing a general visual inspection of the seabed. At the end of the test, PHINS was switched back to operate in INS + USBL mode. This reduced the position error to the initial values. Although the ROV paths during the tests were different, the propagation of the error behaves nearly equal. As shown in the figure, the position error is approximately 2 m after 2 min, 12 m after 5 min, and 40 m after 9 min. Since PHINS is a commercial system, the error propagation model is not known, thus it was necessary to measure error propagation and model the error propagation function. The dashed lines in In the next section, the performance of the vision-based pose estimation system used during the trials is to be compared with the USBL data. If comparable, the visually estimated pose data could be used as PHINS position update. Additionally, the estimated position error is to be reduced using intermittent position fix.Position error estimates provided by PHINS are based on the INS accelerometers and gyroscopes theoretical models, thus are not accurate physical measurements. Although the relative difference of the initial position error was small across three tests initially, the difference grows exponentially after longer periods of time, as shown in A visual position estimation method for subsea localization is described in this section. Since the method presents an upgrade of the method used during field trials, more detailed explanation of the image acquisition, camera calibration, and pose estimation procedure can be found in . Prior tGiven the coordinates of light beacons The output of the visual pose estimation is the homogeneous transformation between the light marker and camera This method showed excellent pose/position estimation when the distance between the marker and the camera is less than 3.5 m, and sufficient accuracy for navigation when the position is estimated from a more significant distance out as far as 10 m distance to marker . As showThe corrected position of the marker in the camera coordinate frame is calculated as: As discussed in the previous section, the position estimation accuracy in pure inertial mode depends on the accuracy of the accelerometers and the FOGs, and accuracy of the initial position measurement. While the accuracy of the IMU data depends on the quality of the sensor, thus cannot be improved, and more accurate position estimation enables better accuracy of the position measurement.The methods presented in After the TMS deployment, the ROV was docked and latched into the TMS. Based on the known ROV position within the TMS when docked and latched, the ROV navigation system was used to measure the TMS position and the orientation in the world frame. The position and the orientation of the light marker in the TMS frame were measured prior to the deployment. Thus, the position of the light marker in the world frame was derived.As shown in z-axis is estimated by calculating the relative distance between the light beacon image coordinates, the position estimation on the z-axis is less prone to error measurements. In addition, the error on the z-axis is less affected by errors in relative heading The error in The experiment showed that visual pose estimated data are comparable with data acquired by PHINS operating in highest precision mode. The vision system performed well in good visibility up to 10 m from the target using light beacon-based position marker and with the standard deviation less than 0.5 m.The ROV position and relative heading in the M frame during a dynamic test are shown in However, to achieve more accurate camera pose estimation throughout the whole distance range, the IMU angle measurements have to be used. As shown in The vehicle has no USBL onboard and is travelling at cruising speed with navigation system based solely on INS.During the trial, the vehicle DVL system is disabled in order to effect a faster integration drift in INS system over time than just 10 minutes operation with DVL.The vehicle operates in a structured environment.Approximately halfway to the docking station, there is a known landmark which can be recognized with the onboard vision system, and the position and orientation of landmark is known in world frame.The experiment simulates a hypothetical scenario of the autonomous vehicle completing a dock at docking station within resident field. The initial conditions are as follows:The experiment starts at time T0 with the ROV docked and the navigational system running in USBL + INS mode. The INS position standard deviation is low. After the position STD reached a minimum value of 0.26 m, PHINS was switched to operate in pure inertial mode, which simulates the USBL signal loss. The ROV was then piloted for approximately 10 minutes in the area around the TMS. Since the propagation of position error in pure inertial mode does not depend on travelled distance or speed, to reduce the risk of damaging the vehicle, the ROV was piloted in relatively close proximity to the deployed TMS. The black dashed line shows PHINS estimated position throughout the experiment while the grey shaded area presents the corresponding position standard deviation. During the experiment, the ROV position was simultaneously measured with the USBL system. The blue continuous line shows the ground truth, USBL measured ROV position with corresponding position standard deviation. To avoid possible noisy measurements and achieve highest precision, the supporting vessel with the USBL transponder was positioned in direct ROV line of sight at all times. Due to the exponential nature of the position error propagation, the position error propagates slowly at the beginning. As shown in the figure, at time T1 after approximately 5 min, the PHINS position standard deviation is around 10 m, reaching over 40 m after 10 min at T4. At time T4 the PHINS system was switched to operate in INS + USBL mode, thus the position error standard deviation (gray shaded area) dropped to an initial value, and the ROV position was corrected. The ROV was then docked at T5, and the experiment finished.Throughout the experiment, the position of the ROV was visually estimated two times. Between time T1–T2, which presents ROV passing by a known landmark, and between T3–T5, upon reaching the docking station. The magnified section of The influence of intermittent, vision based position update on position error estimation was further investigated. Since PHINS 6000 does not provide a designated input for a visually acquired position update, the visually estimated ROV position between T1–T2 and T3–T5 could not be fed into the system Kalman filter. However, the known initial position, and the error propagation function modelled in This paper shows experimental results of a vision-based localization system for the resident ROVs/AUVs which can be utilised to eliminate drift error from on-vehicle inertial navigation system. The proposed system is developed around the standard equipment found throughout the ROV sector. The vision estimated pose based on active light-based marker recognition showed good overall performance and is comparable with the measured USBL ground truth position. While INS operates without USBL/LBL, the visually estimated pose could be used as a position fix, thus reducing the position error caused by position drift.The system has been tested in the North Atlantic Ocean during trials in January 2019. The propagation of position error in pure inertial mode was measured and modelled as a function of time and initially estimated position error. This function is used to simulate a family of possible position trajectories in the case that vision-based position fix is acquired during the test. The visually-based pose estimation method, with IMU relative angle correction, was used to determine the relative pose between the ROV and deployed subsea asset. The results showed that the proposed system performed well and can improve the ROV/AUV localization underwater. The performance of the vision system depends on the water turbidity, in clear water providing up to a 10 m range and solution results in a low cost and stable platform for localization while not being prone to noise pollution as acoustic navigation systems. With the high water turbidity, the system operation range becomes limited and the UUV navigation purely relies on the acoustic based pose estimation technology.High precision INS system with DVL aiding provides a strong platform; however, with the complexities involved in AUV based IMR activities, this is not accurate enough for the transition of resident systems between assets or for close quarter operations on subsea installations. The position drift of such configuration is 0.1% of the travelled distance. However, in close proximity to the marker, visual pose estimation is shown to be a reliable system for an absolute position fix. Since resident ROVs/AUVs operate in the structured environment, this low-cost solution can provide an alternative and more cost effective solution to high-maintenance acoustic-based positioning systems.Future work on this project is investigating the input of real-time 3D dense reconstruction and tracking system, known as SteroFusion , as sens


With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets. The size of the scientific data sets is increasing rapidly with ever-increasing computing capabilities. Modern-day supercomputers can generate data in the order of petabytes and soon we will enter the era of exascale computing ,2. As thSince the data triage is going to be a necessary step in the near future to facilitate flexible, exploratory, and timely post-hoc analysis, the important question now is that how the data should be reduced such that the stored subset of the data retains important information about the scientific simulations and is able to answer the queries performed by domain scientists. Large-scale simulations nowadays output data sets containing multiple variables over time. During the analysis phase of such multivariate data sets, application scientists often explore two or more variables simultaneously instead of looking at each variable individually to investigate the modeled physical phenomena in their simulation. For example, to understand the complex turbulent chemical processes of combustion, scientists have acknowledged that the exploration of multiple variables together is essential and analyzing variables individually is not sufficient . By explAs previous researchers have shown, relationships among multiple variables in the scientific data sets can be complicated ,12,13 anWe propose a new multivariate association-driven data sampling algorithm for large-scale data summarization.Given a user-specified sampling fraction, we use pointwise information measures and statistical distribution-based sampling techniques to generate a sub-sampled data that preserves the important multivariate features.We perform a detailed qualitative and quantitative study to demonstrate the efficacy of the proposed sampling scheme.In this work, we propose a multivariate data summarization technique that selects a small fraction of the original data set  considering multiple variables simultaneously and picks data points with higher fidelity from the regions where the selected variables show a strong association. The benefits of sampling-based data representations for a single variable has been shown by the researchers in the past ,23,24,25In computer graphics and visualization, information theory ,29,30 haSampling has been a very widely popular approach for selecting a subset of values from a given population. Visualization researchers have used sampling primarily in the context of data reduction such that fast data analysis and visualization can be performed. A stratified random sampling based scheme was proposed by Woodring et al.  where thMultivariate data analysis and visualization have historically been very important for researchers. A study of local correlation coefficients among multiple variables for visualization purposes was provided by Sauber et al. . For mulIn this section, we briefly discuss existing generic sampling techniques and then introduce the proposed multivariate association-driven sampling algorithm. Statistical data sampling has been shown to be effective in summarizing and analyzing data sets by researchers in the past ,63. One s  having a higher statistical association. Information theoretic measure pointwise mutual information (PMI) allows us to quantify such shared information. Given two random variables X and Y, if x is an observation of X and y for Y, then the PMI value for the value pair x of X, y of variable Y and, x and y have higher information sharing between them. If x and y do not have any significant information overlap then x and y are considered as statistically independent. Note that, the mutual information X and Y [To perform multivariate statistical association driven sampling, characterization of importance of each data point is essential. In the multivariate data set, each data point has a value tuple consisting of values from each data variable associated with it. Consider a bivariate example where nd Hanks  for the  X and Y .(2)I prIn the above section, we presented pointwise mutual information (PMI) and a generalized extension of it which allows us to quantify the importance of each data point in terms of their statistical association considering multiple variables. In the following, we propose a novel sampling algorithm, which uses such pointwise information measure as a sampling criterion such that multivariate statistical association driven sampling can be done. Using pointwise information assigned to each data point, the proposed sampling scheme accepts data points with higher likelihood when the pointwise information value is high indicating a high statistical association. As a result, the sub-sampled data set is able to preserve the multivariate association with higher fidelity and facilitates efficient visual query and analysis.N is the total number of data points. In order to sample data points using their pointwise information, in this work, we use a multivariate distribution-based approach. Consider a bivariate example, where X and Y are two data variables using which sampling will be done. Firstly, the joint probability distribution of these two variables is estimated using a histogram. The univariate histograms of variable X and Y can be estimated by marginalizing the joint histogram. Since we are considering two variables to describe the sampling algorithm in this example, the joint histogram, in this case, is a 2D histogram. Each bin center in this 2D histogram represents a value pair X and Y, the PMI value for each value pair Given a user-specified sampling fraction α 0<α< as an ins drawn from a standard uniform distribution n data points according to the user-specified sampling fraction B is the number of histogram bins. The scaling factor The above sampling technique is similar in spirit to the importance sampling algorithms in statistics ,67 whereEquation  can be uAn example of the proposed sampling algorithm is provided in In this section, we present the results of the case studies using several scientific simulation data sets to show the effectiveness of the proposed sampling algorithm. To demonstrate the applicability of the sub-sampled data set, in this work, we employ three important analysis tasks: (1) multivariate query-driven analysis, (2) reconstruction-based visualization of sampled data sets, and (3) Multivariate correlation analysis. While computing the multivariate histograms we studied the impact of a number of bins by varying it from 64 to 256. As the number of bins increases, the multivariate histogram becomes more refined as can be seen from P⩾ x AND Q⩽ y, we first extract the data points that satisfy this query. To visualize the results interactively, we use point rendering techniques to directly visualize the query results in the spatial domain. It is to be noted that, since the proposed sampling method prioritizes the data samples based on their PMI values, which reflects the existence of the multivariate association, the proposed technique aims at primarily preserving such statistically associated regions with a higher precession compared to the regions in the data set where the statistical association is weak. Below we present results of QDV from several multivariate scientific data sets and demonstrate the effectiveness of the proposed method.Query-driven visualization (QDV) is a well-known technique to understand and discover multivariate features in scientific datasets ,14,60. QWe first apply the QDV analysis to Hurricane Isabel data set. Hurricane Isabel data set is used to study the impact of the hurricane Isabel on the coastal regions of the United States. The dataset is courtesy of NCAR and the U.S. National Science Foundation (NSF) and was created using the Weather Research and Forecast (WRF) model. The spatial resolution of the data is he storm ,10. TherIn Next, the QDV analysis is applied to a Turbulent Combustion data set. This dataset is a turbulent simulation and consists of 5 variables. Among them, the mixfrac variable is an important variable and denotes the proportion of fuel and oxidizer mass and this value generally provides the location of the flame where the chemical reaction rate exceeds the turbulent mixing rate ,12. The e region , we perfg method c generatFinally, we apply the query-driven analysis to the Asteroid impact data set. The Deep Water Impact dataset  represenmosphere . Since tThe results of the query-driven analysis are presented in d method c is simiJaccard index [P and Q are two finite sets. The value of P and Q are identical. In our study of the query-driven analysis, set P is the point set resulted from the query applied on the raw data and Q is the point set resulted from either the proposed method or from the query applied on the randomly sampled data. Higher values of Jaccard index indicate more accurate query results. In To study the accuracy of the query-driven analysis quantitatively, in this work, we have used the similarity measure rd index . The JacBesides query-driven analysis, to visualize the data at its entirety at full resolution, we also perform reconstruction of the data from the sub-sampled data points. In this work, we have adopted a linear interpolation based technique for data reconstruction such that the features in the data can be effectively visualized using volume visualization techniques. To linearly interpolate the data, first, a 3D convex hull is generated using all the sampled points. Next, the points are converted to a polygonal mesh by using Delaunay triangulation. Finally, for each grid point in the reconstruction grid, the value is calculated by linearly interpolating scalar values from the vertices of the simplex that encloses the current grid point. After reconstruction, the traditional raycasting-based volume rendering algorithm is used to visualize the features in the data set. In the following, we first present the visualization of the reconstructed data for several multivariate data sets and then provide a quantitative image-based study of the visual quality of the results to demonstrate the efficacy of the proposed method.The reconstruction based visualization for mixfrac and Y_OH fields of Turbulent Combustion data is shown in echnique b and theechnique c produceFinally, we show the reconstruction-based visualizations of tev (temperature) field in a, b, and c are three parameters used to assign the relative importance to the three corresponding components in the measure and the default value for those parameters are set to 1 indicating equal importance. Using SSIM, the comparison between the images are done on local patches first and finally the mean values of all SSIM values from all the patches are computed which represents the final SSIM between two images. Besides SSIM, we have also used Mean Squared Error (MSE) as our second image quality comparison measure. Note that higher values of SSIM indicate better similarity between the test image and the reference image and since MSE estimates an error quantity, lower values are considered more accurate for this measure. In our study, the raw images are considered as the reference image and images produced by the proposed sampling algorithm and random sampling are compared against the reference image.After the above qualitative analysis of reconstruction-based data visualization, in this section, we provide a comprehensive study of the quality of the images generated by both the proposed sampling method and random sampling method. To study the quality, we performed an image-based comparison of the results obtained from both the sampling technique to the ground truth generated from the full resolution raw data under varying sampling fractions. Note that, for the generation of images, the same rendering parameters are used for each data set. To study the image quality quantitatively, we have used the Structural Similarity Index (SSIM) . SSIM isThe results of the image-based quality comparison for Hurricane Isabel data set is presented in Pearson’s correlation coefficient, which measures the linear correlation between variables. In addition, since the variables can have non-linear dependence, we have also used distance correlation [In this section, we evaluate the proposed method in preserving the multivariate association among selected variables. Since the proposed PMI-based sampling technique prioritizes data points based on their shared information, the technique aims at preserving the relationship between variable combinations in the strongly associated regions. More specifically, the proposed technique aims at recovering back the statistical association in the important regions where a strong local dependence among variables exists. Note that, as shown above, these strongly statistically associated regions often indicate important scientific features in the data set where multiple variables interact with each other. For example, in Hurricane Isabel data set, we have shown that the low Pressure valued regions  and moderate to high wind velocity regions show the important feature “hurricane eye”. The proposed technique in this work aims at preserving such regions with higher fidelity such that the multivariate dependence among variables in such regions can be recovered. Therefore, to evaluate the quality of the preservation of statistical association, we have used two measures in this work. The first measure is relation  to estimThe first evaluation is done on the reconstructed data set by specifically focusing on the important feature regions of the data sets. In In this work, we propose a new multivariate statistical association driven data sub-sampling algorithm that aims at preserving the statistically associated regions in the multivariate data sets with higher accuracy in the form of a reduced sub-sampled data set. To achieve this, the proposed technique first uses pointwise information theoretic measures to compute the strength of statistical association for each data point in the spatial domain and then uses a multivariate distribution-based approach to sample data points according to the strength of their multivariate association. Therefore, such a sampling technique produces a sub-sampled data set where stronger statistically associated regions are sampled densely compared to the regions that demonstrate weaker association among selected variables. We show several visualization and analysis tasks such as (a) multivariate feature query, (b) reconstruction based feature visualization and (c) recovery of multivariate correlation and dependence for important regions of interest in the data sets where the proposed sampling technique can be preferred over the traditional random sampling algorithms. This is primarily due to the fact that the random sampling technique samples the whole data set randomly and the importance of all the data points are equal. Hence, it does not sample any region densely.However, when experts want to preserve the overall data distribution and statistical quantities like mean, standard deviation, random sampling technique can be preferred over the proposed method. Furthermore, as we have observed from Another potential limitation of the proposed method in its current state is the use of multivariate histograms whose dimension increases with the number of variables used during sampling. When the number of variables is large, creating a high-dimensional histogram can be challenging and also memory consuming. Therefore, to overcome this issue, we plan to first cluster the variables using their shared information content into smaller sub-groups as was proposed in  and thenIn addition to the above goals, another future task is to apply the proposed sampling method to the distributed parallel environment such that the sampling algorithm can be run in situ while the simulation is running and the generated data resides in the supercomputer memory. This will lead to an effective in situ statistical sampling-based data summarization technique and the reduced sub-sampled data can be explored timely in the post-hoc analysis phase. In order to perform the sampling in a distributed environment, we need to compute the global joint data distributions and we plan to leverage the optimized multivariate histogram computation technique provided in  for estiIn summary, this paper presents a new multivariate association driven data sampling technique for summarization of large-scale scientific data sets. We use pointwise information theoretic measures to assign importance to data points based on their statistical association and then perform data sub-sampling according to such multivariate importance criterion. The proposed sampling algorithm is applied to several scientific data sets to conduct multivariate feature analysis. Through comprehensive quantitative and qualitative query-driven analysis and reconstruction-based visualization, the usefulness of the proposed sampling algorithm is justified.


Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters  were measured using the body composition tester  based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity. As the number of fat people increases, obesity has become a worldwide epidemic. The number of patients with obesity has exceeded the number of patients who have contracted infectious diseases or who suffer from undernutrition. It constitutes a severe menace to human health , as overThe image inspection techniques for predicting abdominal adiposity and obesity include: computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and dual-energy X-ray absorptiometry (DXA). Han  analyzedBIA is widely used in researches due to the merits such as non-invasiveness, convenience, and low costs. Eickemberg  predicteIn summary, mathematical models based on these two types of technology have a better prediction effect. However, imaging has obvious shortcomings, such as radiation, high cost, and limited testing space. BIA, due to the merits of non-invasiveness, non-radiation, convenience, and low costs, has gradually been applied to researches on human abdominal adiposity. However, most of the current BIA based methods depend on specific samples and are difficult to solve the problem of small sample size, whose prediction accuracy is unsatisfactory.(1)The application of particle swarm algorithm (PSA) to optimize the parameters of SVM, which obtains the improved SVM (iSVM);(2)An abdominal adiposity classification model is developed using iSVM to process the data obtained from BIA;(3)The simulation experiments were designed to evaluate the prediction accuracy and effectiveness of abdominal adiposity classification in two frequency bands.In this paper, we put forward a method of abdominal adiposity prediction based on BIA and improved support vector machine (iSVM), in order to improve prediction accuracy and solve the problem that the model is affected by the sample size. The main contributions include:The study was approved by the Ethics Review Board of Lingnan Normal University. Ninety eight (98) men and 102 women were recruited for the study via posted notices and word-of-mouth and informed consent was obtained. The sample consisted of 200 healthy Chinese who were instructed to fast for 4 h and avoid heavy exercise, alcohol, or caffeine for 10 h prior to the visit. Participants have three clinical characteristics: normal, overweight and obese. They were measured human physiological parameters, including BMI, weight, body fat mass, and human electrical impedance, on a human body composition tester based on BIA technology.The first step of constructing the prediction model is to select the key characteristics of human abdominal adiposity. We used the body subdivision eight-segment impedance model  to obtaiWe selected the key characteristics by using a human characteristics selection algorithm combining filtering and clustering . We firsAs mentioned above, the data were included in the set SVM is a classifier with sparsity and robustness which uses hinge loss function to calculate empirical risk and adds regularization terms to the solution system to optimize structural risk and its decision boundary is the maximum margin hyperplane solved for the learning sample. More importantly, SVM can perform non-linear classification by the kernel method, which is one of the common kernel learning methods. Therefore, the classification model is based on the SVM using RBF as the kernel function in this paper. The range of the boundary parameter C depends on the range of tolerable error, which is the mean of the sampling error and the structural risk. The range of the kernel parameter depends on the range of the training samples. At present, the globally optimal kernel parameter is obtained by cross-validation (CV), which requires a lot of time to achieve high prediction accuracy. We thus decided to optimize the SVM parameters by the PSA.Particle swarm algorithm is a random search algorithm based on swarm cooperation, which is developed by simulating the foraging behavior of bird swarms. The PSA reflects social sharing of information. The to-be-optimized parameters are called “particles,” which constitute a population In the training of iSVM, every parameter of the training samples are taken from the selected characteristic parameters in Step 1: Import and then normalize the data. Mix the data sampled from the obesity, overweight, and normal populations. From the mixed data randomly pick 100 data to form the training set and another 100 to form the test set.Step 2: Initialize the particle swarm. Let the number of particles be 100, the same as the number of training samples  errors of all the three models, namely the square root of the sum of the multiple square errors. The RMS prediction errors of the three models in the frequency bands 1 KHz and 250 KHz are presented in From the tables one sees that the RMS error of the prediction by the iSVM is apparently smaller than that by the traditional regression model and the classification error of the iSVM method is apparently smaller than the simple waistline measurement method. The time consumption of iSVM is a little more, due to the computation and training that were necessary to improve the accuracy.In order to obtain more effective prediction than traditional methods such as waist measurement and BMI, image detection and BIA technology are mainly used to predict abdominal obesity. However, there are some obvious disadvantages to image detection technology. The use of CT is particularly widespread, but the radiation does harm to the human body to a certain degree. MRI has no radiation problem, but it is expensive and has site-specific constraints. Ultrasound imaging is non-invasive and cheap, but its diagnosis precision depends on operating experience and equipment performance. X-ray imaging is simple and inexpensive, but it also has the radiation problem. Therefore, we chose to use BIA technology for abdominal prediction research.The proposed model based on BIA technology has the advantages of harmlessness to humans, convenience and low cost. What’s more, in order to solve the problem that it is difficult to predict invisible obesity directly, we considered the abdominal electrical impedance as a predictor. Using abdominal impedance as a variable in the prediction model can more accurately predict abdominal obesity because the value of abdominal impedance changes as it becomes fatter. From the comparison with the waist circumference model, the proposed model has higher prediction accuracy, which can more effectively predict hidden obesity.It is well known that support vector machine is often used to solve small sample size problems and PSA is an optimization algorithm. Therefore, we suggested a prediction method based on the combination of particle swarm algorithm and support vector machine for obtaining a prediction model suitable for small sample size and high accuracy. In comparison with separate support vector machine model and traditional regression model, the proposed model shows better prediction results. The reasons why the single SVM model and the traditional regression model show poor performance are that the single SVM model lacks optimized parameters and the sample size for the traditional regression model is insufficient. Accordingly, the method in this paper offers great advantages in terms of safety, cost, hidden abdominal obesity prediction, small sample size prediction and prediction accuracy.In this paper, we put forward a new model of classifying abdominal adiposity. The parameters of the SVM, which uses the RBF kernel functions, were optimized by the PSA so that the SVM can achieve a higher modeling accuracy. The experimental results demonstrate that the present model has high accuracy and can effectively predict abdominal adiposity. What is more, the method in this paper has the advantages of more effective prediction effects, radiation-free, cheapness and convenience. Therefore, the present study has provided a new model for the practical abdominal adiposity prediction. Finally, a combination of image detection technology and BIA technology will be considered to predict abdominal obesity in the future research work.


In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization. Rolling bearings are prevalent components in rotating machinery for modern industrial applications . MoreoveTraditional fault diagnosis method such as support vector machines (SVM) is often unable to automatically extract high-level features of bearing data and require too much prior knowledge. Moreover, the selection of features directly affects the effect of fault diagnosis, and it is difficult to apply to massive data . One of Although CNN used as the backbone network in this paper also has the above problems. However, the research in this article is dedicated to improving these issues. CNN  has beenBased on the thinking, this paper proposes a model named adaptive long sequence convolutional network (ALSCN) which can balance diagnosis accuracy and big data requirements without deepening the model . The maiAn end-to-end ALSCN is proposed to bearing fault diagnosis which performs well under strong noise and different loads environment simultaneously. In addition, ALSCN can directly act on longer original signals, thereby reducing the workload of manual signal preprocessing.The multi-filter-layer based on improved atrous spatial pyramid pooling (ASPP)  is develThe Bayesian optimization algorithm are applied to optimize the hyperparameters for reducing the time of manual parameter adjustment. Furthermore, after removing the fully connected layer, ALSCN has an ideal cost in parameter calculation.Firstly, by visualizing the feature learning process between different layers of the network, the internal mechanism is explored. Then, the different results which are caused by the different order of modules and pooling operations are discussed and explained. Finally, the best structure for the ALSCN is given.Based on the bearing fault data set of Case Western Reserve University (CWRU) and the mechanical fault prevention technology (MFPT), the ALSCN model proposed outperforms SVM, CNN, RNN, DBN, and BP neural network models.The rest of the paper is organized as follows. In A typical convolutional neural network structure is shown in The receptive field is defined as the area where the convolutional neural network features can see the input image. The larger the receptive field is, the farther distance of characteristic relationship between objects that network can learn. In addition, the deeper layer of a network is, the greater range that network can perceive. For long time sequences, the distribution of key classification points is more random and changeable, and the important classification information which is captured in the deep neural network will also be more abstract. Therefore, for capturing key classification features on long time sequences more effectively, expanding the receptive field of the network is a wonderful method. The calculation formula of the receptive field can be expressed asAt present, there are two main methods for increasing the receptive field . (1) BuiIn the field of bearing fault diagnosis, the convolutional network will cause insufficient feature extraction due to continuous pooling operations. Obviously, it will affect the accuracy of diagnosis. To overcome the above problem, the concept of dilated convolution is introduced in this section. A parameter called dilated rate is presented by the dilated convolution to define the distance between the values. In dilated convolution, the elements of the convolution kernel are spaced, and the size of the space depends on the dilated rate. A principle of expanding the receptive field on a one-dimensional vector is shown in In The calculation method of the dilated convolution is the equal interval sampling, and the convolution result obtained by one layer always comes from an independent set of the previous layer. Hence, it always causes a certain problem of information loss. Moreover, the dilated convolution samples the input signal sparsely. This makes no correlation between the key features of the long-time sequences obtained by convolution, thereby affecting the classification result. Thus, the spatial pyramid pool and dilated convolution are introduced here to solve the above problem. The spatial pyramid pool was first introduced to CNN by He et al.  to meet The computational cost of CNN is relatively high, and it may take plenty of time to train on traditional platforms. Moreover, no CNN model can best generalize all data sets . The metGiven the objective function, random sampling is performed in the parameter space.Obtain the initial objective function distribution, and then continuously search for the optimal solution of the objective function based on historical information.Iterate continuously until the distribution fitted by the sampling points is roughly the same as the true objective function. In order to fit the relationship between parameter selection and objective function more comprehensively, Bayesian optimization puts forward the idea of probabilistic surrogate model. Bayesian optimization consists of two parts, the probabilistic surrogate model and the acquisition function.The update of the probabilistic surrogate model is determined bySince the non-parametric probabilistic surrogate model is more flexible and easier to expand, it can better describe the unknown objective function. Among them, the Gaussian process with strong fitting performance is the most widely used .The acquisition function is the basis for the targeted search for the next evaluation point in the parameter space. This article selected PI (Probability of Improvement) as the collection function. PI indicates that the next sample point collected may improve the possibility of the optimal objective function, as shown in the Equation (3).The schematic architecture of bearing fault diagnosis method based on the proposed network is illustrated in The structure of the convolutional block consists of a convolutional layer, batch normalization (BN) and rectified linear unit (ReLU). In the convolutional layer, the local area of the input is convolved through the kernel of the filter. Subsequently, under the action of the activation unit, the output features are effectively generated. BN is used behind the convolutional layer to make the training time shorter and reduce the offset of the internal covariance. In recent years, ReLU as a vital part is widely used in the example of activation unit. Because ReLU can increase the representation ability of the network and make the learned features easier to divide.The model structure proposed in this paper is shown in Facing the disadvantage that the dilated convolution will cause the loss of information. Moreover, the problem of weak spatial correlation between the long time series features extracted using standard convolution. This paper improves the attentive spatial pyramid pooling (ASPP) module and proposes a multi-scale max pooling module.Due to the excessive number of layers of the inception series networks, the network will be difficult to train or even degraded, and it is also prone to overfitting . HoweverThe previous layer of the module is a convolution block with a convolution kernel size of one and a step size of one. Then, the output of the convolution block is used as the input of the module. After that, one dilated convolution  is as follows:The signal with a SNR between −14 dB and 0 dB is defined as a weak signal. According to Equations (5) and (6), the Gaussian noise is added to all samples to synthesize composite signals with different SNR. As given in Afterwards, based on the data set D, a comparison experiment is conducted with the accuracy of other models. The comparison results are demonstrated in It can be seen from This section conducted experiments on the four data sets to further prove the generalization of ALSCN under different loads. According to the confidence level of 95, the corresponding confidence interval is given to better rule out the contingency of experiment. The confidence interval calculation equation is as follows.By observing In addition, the MFPT bearing fault data set  was alsoTest accuracy of different models under different SNR on MFPT are shown in An end-to-end ALSCN model is proposed to solve the problem that the accuracy of traditional and deep learning models decreased on longer signal under noise. Firstly, the fault diagnosis method based on ALSCN removes the process of manually extracting features. Secondly, it can adaptively adjust hyperparameters for different application conditions. Furthermore, the article developed two improved multi-scale modules to greatly preserve the spatial correlation on longer signals and make up the loss of detailed information under strong noise. This enables ALSCN to efficiently diagnose longer time series under strong noise of practical engineering application. The proposed model is validated for the robustness and generalization ability of the algorithm on CWRU and MFPT data set. The diagnosis results are compared with the traditional method SVM and deep learning methods CNN, DBN, BPNN, and RNN. The average accuracy of the ALSCN is improved by 9.34%, 12.27%, 24.17%, 22.59% and 42.02% on the four data sets of the CWRU, respectively. Under the strong noise (SNR = −5dB) on data set D, the accuracy of the ALSCN is increased by 8.69% compared to the well-performing CNN. On the MFPT data set, it also increased by 7.51% and 5.5%, respectively, compared with CNN and RNN. The method proposed in this paper has certain practical value and application prospects for the research of fault diagnosis based on long time series under noise.In future work, author will explore the hardware implementation of this model and the real-time classification of more faults in rotating machinery.