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Maximal lactate steady state (MLSS) is accepted as the gold standard method to determine the upper boundary of heavy exercise domain, while critical power (CP) is one of the most practical ways to estimate the lower boundary of severe exercise domain. However, it has been recently discussed that the upper boundary of heavy-intensity exercise domain does not equal to lower boundary of severe-intensity exercise domain. Conversely, the best individual fit method (CP fit) has recently been suggested as an accurate estimation of CP. However, there has not been a comparative evaluation of CP fit versus MLSS. The aim of this study was, therefore, to focus on the comparison of CP fit with MLSS. Eleven well-trained male cyclists participated in this study. Following the determination of maximal oxygen uptake, MLSS was determined through the performance of standard 30-minute constant work rate exercises performed on separate days. Cyclists performed four constant-load exercises lasting 2–15 minutes on different days. The data were then fitted to mathematical models to obtain CP fit. Bland-Altman plot with 95% limits of agreement (LoA) was used to measure agreement between CP fit and MLSS. According to the findings, CP fit overestimated the work rate corresponding to MLSS (300 ± 38 W vs. 265 ± 39 W; P = 0.001; effect size = 3.28). Bland-Altman results showed high LoA (14 to 55.9 W) and very high bias (bias = 35 ± 10.7 W; P = 0.001; r : 0.96) between CP fit and MLSS. CP fit was not identical for MLSS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive sliding mode observer (SMO) over wide speed range for the sensorless control of a brushless DC motor (BLDCM) system. Designed with a sinusoidal saturation switching function whose boundary layer can be adaptively adjusted according to varying speed through fuzzy algorithm, the SMO is able to improve the estimation accuracy of the back electromotive force (EMF) as well as the rotor position and speed with properly selected parameters under both low speed and high speed circumstances. The asymptotic stability of this new strategy is verified, and its effectiveness and superiority to the conventional SMO are illustrated via simulations on MATLAB and experiments on a BLDCM sensorless control platform. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive approach to directly control untripped vehicle roll-over. First, a novel real-time estimation scheme is designed to provide the vehicle roll angle using an observer on a combined model of vehicle kinematics and roll dynamics. This angle is used in an integrated directional and roll dynamics model for the prediction of vehicle states and roll-over index. The roll-over prevention objective is specified as a soft constraint to ensure persistent feasibility. If the controller foresees impending vehicle roll-over, it intervenes and reduces the roll-over index by torque vectoring. Software simulations are used to assess the effectiveness of the proposed roll-over control method in the industry standard fishhook maneuvers. In addition, the accuracy and performance of the suggested estimation and control algorithms are verified in double-lane change and flick maneuvers on dry asphalt with an instrumented test vehicle. The results show accurate estimation of the vehicle roll angle and excellent control on roll-over index with the proposed predictive roll-over controller. [ABSTRACT FROM AUTHOR]

Physical Protection Systems (PPS) are used to protect critical facilities and prevent against adversarial intrusion. The insider threats of PPS must be considered when analyzing the effectiveness of PPS. On the basis of the normal approach termed “Estimate of Adversary Sequence Interruption, EASI”, a novel method named “Estimate and Prevention of the Insider Threats, EPIT” was proposed for the specific estimation of insider behaviors. According to failure mode and effects analysis (FMEA) method, the EPIT method adequately considers the common failure causes of protective devices to analyze the insider threat to the effectiveness of PPS. By the EPIT method results, a reasonable management and rights allocation of staffs can be figured out to mitigate insider threats. [ABSTRACT FROM AUTHOR]

The paper presents a new approach to enhance the fault detection, and location determination based on travelling waves using Fast Discrete S-transform (FDST) for TCSC compensated lines connecting to wind farm. The FDST is applied to the modal components of measured currents at each terminal to detect the arrival time of the first travelling wave (transient) produced by the fault. The proposed method includes detecting the terminal with fastest arrival time of wave to identify the faulted section and estimate the fault location by using proper distance index. The simulation results have demonstrated good performance of the proposed scheme under different fault locations, fault resistances, fault inception angles, fault types, faulted sections, variations in TCSC parameters and changing wind speeds. The performance validation on the real-time digital simulator (RTDS) platform enhances the applicability of the proposed protection scheme for the TCSC based compensated line integrated with wind farm. The performance comparison with the conventional travelling wave fault location algorithms using Continuous Wavelet Transform (CWT) shows potential ability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel filter for jointly tracking and classification (JTC) of maneuvering extended targets using the standard probability hypothesis density (PHD) framework. For an extended target, the extended state that describes the target size, shape and orientation is also estimated, in addition to the kinematic state. Assuming that the target size information is known in advance, the presented filter can classify the extended target based on different sizes, instead of based on different kinematic motion modes in point target tracking. By utilizing the known target size information, the presented filter can contribute to a better extended state estimation while classifying, and how a good classification result can improve the estimation is mathematically analyzed. Simulation results show that the presented filter simultaneously provides a superior tracking performance and the correct classification of multiple extended targets, compared to the gamma Gaussian inverse Wishart PHD (GGIW-PHD) filter. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for finding the optimal route for a specific train from a station to another one in the Spanish railway network (or any railway network involving different incompatible features like gauges, electrification and signaling systems). The complexity of the Spanish railway infrastructure makes it difficult to give an estimation of the fastest route of a train from a given station to another. Indeed, very unintuitive situations may happen. The problem of finding fastest routes is typically modeled by a graph where nodes represent stations and edges represent railway sections. However, this approach is not suitable for the Spanish railway network. In order to solve the problem of calculating the fastest routes, we will propose here a novel approach based on modeling the railway network through a different graph whose nodes represent railway sections. [ABSTRACT FROM AUTHOR]

The problem of characterizing and estimating the instrumental or radiometric noise of satellite high spectral resolution infrared spectrometers directly from Earth observations is addressed in this paper. An approach has been developed, which relies on the Principal Component Analysis (PCA) with a suitable criterion to select the optimal number of PC scores. Different selection criteria have been set up and analysed, which is based on the estimation theory of Least Squares and/or Maximum Likelihood Principle. The approach is independent of any forward model and/or radiative transfer calculations. The PCA is used to define an orthogonal basis, which, in turn, is used to derive an optimal linear reconstruction of the observations. The residual vector that is the observation vector minus the calculated or reconstructed one is then used to estimate the instrumental noise. It will be shown that the use of the spectral residuals to assess the radiometric instrumental noise leads to efficient estimators, which are largely independent of possible departures of the true noise from that assumed a priori to model the observational covariance matrix. Application to the Infrared Atmospheric Sounder Interferometer (IASI) has been considered. A series of case studies has been set up, which make use of IASI observations. As a major result, the analysis confirms the high stability and radiometric performance of IASI. The approach also proved to be efficient in characterizing noise features due to mechanical micro-vibrations of the beam splitter of the IASI instrument. [ABSTRACT FROM AUTHOR]

In this paper we disclose two new models for the estimation of the higher heating value of refused derived fuels as an answer to the question – “Are the available models to predict the HHV of biomasses suitable to be applied with an acceptable accuracy to RDFs?”. Based on the carbon and hydrogen content the newly developed equations were used to predict, accurately, the higher heating value of 25 samples available from open database sources and then compared with other 35 equations already introduced by other authors. The models herein presented have demonstrated, within the test group used to lead to very low average absolute and bias errors. The models were then validated with samples obtained locally, showing that the results were in line with that obtained for the test group. [ABSTRACT FROM AUTHOR]

Supply vessels serving oil and gas installations at sea are a major emission source of greenhouse gases in offshore logistics. Speed optimization strategies applied in tactical vessel planning significantly reduce fuel consumption and thus emissions. Nevertheless, the environmental performance of these strategies in rough weather conditions is difficult to estimate as the duration of supply vessel operations is weather dependent. In this paper we develop a simulation-optimization tool which estimates the average fuel consumption for weekly vessel schedules. The schedules are constructed by using speed optimized vessel voyages which are simulated under different weather conditions. Results of experiments on real instances show that rough weather conditions increase fuel consumption and costs. The application of speed optimization strategies reduces fuel consumption both in winter and summer, but the reduction may be at the expense of a fixed vessel cost increase in the winter season. [ABSTRACT FROM AUTHOR]

Owing to mal-operation of the conventional scheme during high resistance ground fault near tap point, a new faulty section identification and fault localization technique for three-terminal transmission line is presented in this paper. The proposed technique utilizes time-synchronized voltage and current signals from all the three terminals. Initially, fault detection based on estimation of superimposed voltage of tap point with reference to all three terminals has been carried out. Subsequently, utilizing the above three estimated superimposed voltages; faulty section identification criterion is formed. Finally, fault localization i.e. estimation of the value of fault distance and resistance has been performed. The authenticity of the proposed technique has been verified by simulating an existing 400 kV Indian three-terminal transmission network in PSCAD/EMTDC software. The simulation results point out that the proposed technique is able to identify the faulty section correctly. Moreover, it precisely estimates the value of fault distance and fault resistance as the percentage error for fault location and resistance remains within ±1.5% and ±3.5%, respectively. Likewise, its performance remains unaffected during wide variation in fault and system parameters. At the end, comparative evaluation of the proposed technique with the existing protection scheme clearly shows its superiority. [ABSTRACT FROM AUTHOR]

This paper presents an integrated analytical approach to estimate technical losses (TL) of medium voltage (MV) distribution network. The concept of energy flow in a radial MV distribution network is modelled using representative feeders (RF) characterized by feeder peak power demand, feeder length, load distribution, and load factor to develop the generic analytical TL equations. The TL estimation approach is applied to typical utility MV distribution network equipped with energy meters at transmission/distribution interface substation (TDIS) which register monthly inflow energy and peak power demand to the distribution networks. Additional input parameters for the TL estimation are from the feeder ammeters of the outgoing primary and secondary MV feeders. The developed models have been demonstrated through case study performed on a utility MV distribution network supplied from grid source through a TDIS with a registered total maximum demand of 44.9 MW, connected to four (4) 33 kV feeders, four (4) 33/11 kV 30 MVA transformers, and twelve (12) 11 kV feeders. The result shows close agreement with TL provided by the local power utility company. With RF, the approach could be extended and applied to estimate TL of any radial MV distribution network of different sizes and demography. [ABSTRACT FROM AUTHOR]

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization. [ABSTRACT FROM AUTHOR]

In this paper, a novel freeway traffic speed estimation method based on probe data is presented. In contrast to other traffic speed estimators, it only requires velocity data from probes and does not depend on any additional data inputs such as density or flow information. In the first step the method determines the three traffic phases free flow, synchronized flow, and Wide Moving Jam (WMJ) described by Kerner et al. in space and time. Subsequently, reported data is processed with respect to the prevailing traffic phase in order to estimate traffic velocities. This two-step approach allows incorporating empirical features of phase fronts into the estimation procedure. For instance, downstream fronts of WMJs always propagate upstream with approximately constant velocity, and downstream fronts of synchronized flow phases usually stick to bottlenecks. The second step assures the validity of measured velocities is limited to the extent of its assigned phase. Effectively, velocity information in space-time can be estimated more distinctively and the result is therefore more accurate even if the input data density is low. The accuracy of the proposed Phase-Based Smoothing Method (PSM) is evaluated using real floating car data collected during two traffic congestions on the German freeway A99 and compared to the performance of the Generalized Adaptive Smoothing Method (GASM) as well as a naive algorithm. The quantitative and qualitative results show that the PSM reconstructs the congestion pattern more accurately than the other two. A subsequent analysis of the computational efficiency and sensitivity demonstrates its practical suitability. [ABSTRACT FROM AUTHOR]

In this paper we study spatial quantile regression estimation of multivariate threshold time series models. Bahadur’s representations for our estimators are established, which naturally lead to asymptotic normality of the estimators. Simulations and a real example are used to evaluate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

This paper presents a corrected partitioned scheme for investigating fluid–structure interaction (FSI) that may be encountered by lifting devices immersed in heavy fluid such as liquids. The purpose of this model is to counteract the penalizing impact of the added mass effect on the classical partitioned FSI coupling scheme. This work is based on an added mass corrected version of the classical strongly coupled partitioned scheme presented in Song et al. (2013). Results show that this corrected version systematically allows convergence to the coupled solution. The fluid flow model considered here uses a non-stationary potential approach, commonly termed the Panel Method. The advantage of this kind of approach is twofold: first, in restricting itself to a boundary method and, second, in allowing an added mass matrix to be estimated as a post-processing phase. Whereas the classical scheme encounters an acceptable (no numerical oscillation) convergence limit for fluid densities higher than 8 kg/m 3 for the considered case, our corrected scheme is not dependent on fluid density and converges with only 6 iterations. This makes it possible to investigate the dynamic behavior of a 2D foil immersed in heavy fluids such as water. For example, it recognizes that frequency shifting may occur as the consequence of a strong added mass effect. [ABSTRACT FROM AUTHOR]

We investigated the estimation accuracy of synaptic conductances by analyzing simulated voltage traces generated by a Hodgkin–Huxley type model. We show that even a single spike substantially deteriorates the estimation. We also demonstrate that two approaches, namely, negative current injection and spike removal, can ameliorate this deterioration. [ABSTRACT FROM AUTHOR]

Accurate cost estimation is crucial for obtaining ship owners’ orders in shipyards. The classic preliminary estimation methods of ship costs provide only rough estimates of the labor, materials, and equipment based on the overall ship parameters and do not reflect further specifications. This study develops an innovative cost estimation method called the feature-based estimation method that is based on the preliminary specifications to estimate ship costs, including the steel, other main materials, engine, power generator, other core equipment, and labor hours. The method mainly establishes the topology of the relationships between the features by linking the general dimensional parameters and detailed features of the specifications of the designs and cost information to estimate the main cost items of the ship. The features are extracted and transformed into a quantifiable structure. The definitions of the features contains the core context using a small amount of information for the preliminary estimation. Empirical formulas are derived based on the configured cost items in the preliminary design stage. The errors of the estimated total costs are less than ± 7%. Hence, the estimation model is suitable for modern ships. The applications of the model may be more robust for new ships in a future study. [ABSTRACT FROM AUTHOR]

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. A statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and dynamic reconfiguration algorithm for photovoltaic (PV) arrays in order to mitigate negative effects of non-uniform spatial irradiance profiles on PV power production. Spatially dispersed irradiance profiles incident on inclined PV module surfaces at each application site are generated based on real sky images. Models of PV modules are constructed in MATLAB/Simulink based on one-diode mathematical model of a PV cell. The proposed dynamic reconfiguration algorithm operates based on irradiance equalization principle aiming for creation of balanced-irradiance series-connected rows of PV modules. The proposed algorithm utilizes an irradiance threshold to obtain near-optimal configurations in terms of irradiance equalization and number of switching actions under any type of non-uniform spatial irradiance profile. The algorithm provides no limits on the number of PV modules within the array. The reconfiguration algorithm is examined with different irradiance profiles and significant improvements, almost equivalent to the ideal case corresponding to equal irradiance for all panels, are achieved for each shading pattern. The advantages of the algorithm are simplicity and providing significant improvements in array’s power generation alongside with reduced number of switching actions. [ABSTRACT FROM AUTHOR]

The prediction of the added resistance and attainable ship speed under actual weather conditions is essential to evaluate the true ship performance in operating conditions and assess environmental impact. In this study, a reliable methodology is proposed to estimate the ship speed loss of the S175 container ship in specific sea conditions of wind and waves. Firstly, the numerical simulations are performed to predict the added resistance and ship motions in regular head and oblique seas using three different methods; a 2-D and 3-D potential flow method and a Computational Fluid Dynamics (CFD) with an Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach. Simulations of various wave conditions are compared with the available experimental data and these are used in a validation study. Secondly, following the validation study in regular waves, the ship speed loss is estimated using the developed methodology by calculating the resistance in calm water and the added resistance due to wind and irregular waves, taking into account relevant wave parameters and wind speed corresponding to the Beaufort scale, and results are compared with simulation results obtained by other researchers. Finally, the effect of the variation in ship speed and therefore the ship speed loss is investigated. This study shows the capabilities of the 2-D and 3-D potential methods and CFD to calculate the added resistance and ship motions in regular waves in various wave headings. It also demonstrates that the proposed methodology can estimate the impacts on the ship operating speed and the required sea margin in irregular seas. [ABSTRACT FROM AUTHOR]

The problem of asynchronous state estimation for Markov jump neural networks taking into account jumping fading channels is investigated in this article. The phenomenon of channel fadings which occurs between the system and the state estimator is considered and a modified discrete-time Rice fading model with the mode-dependent channel coefficients is adopted. Due to the fact that the modes of system can not be completely accessible to the state estimator at any time, the asynchronous state estimator which can make full use of the partial information available to the state estimator is introduced. By using the mode-dependent Lyapunov functional approach, some sufficient conditions for the existence of asynchronous state estimator of the Markov jump neural networks are given to guarantee the stability and dissipativity of the augmented system. The gains of asynchronous state estimator are given via solving a set of linear matrix inequalities. The merits and effectiveness of the developed design scheme are verified by a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, we investigate L ∞ -error estimates of the bilinear elliptic optimal control problem by rectangular Raviart–Thomas mixed finite element methods. The control variable enters the state equation as a coefficient. The state and the co-state variables are approximated by the Raviart–Thomas mixed finite elements of order k = 1 , and the control variable is approximated by piecewise linear functions. The L ∞ -error estimates are obtained for the control variable and coupled state variable, and the convergence rates of orders O ( h 2 ) and O ( h 3 2 | ln h | 1 2 ) are also gained for the control and state variables and the flux of the state and co-state variables, respectively. In addition, the performance of the error estimates is assessed by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper deals with the identification of errors-in-variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise-free input is an arbitrary signal, not required to be periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed and analyzed in some detail. As some other EIV estimators, this method assumes that the ratio of the noise variances is known. The estimation problem is formulated in the frequency domain. It is shown that the parameter estimates are consistent. An explicit algorithm for computing the asymptotic covariance matrix of the parameter estimates is derived. The possibility to effectively use lowpass filtered data by using only part of the frequency domain is discussed, analyzed and illustrated. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of detecting faults in linear randomized gossip algorithms, where the selection of the dynamics matrix is stochastic. A fault is a disturbance signal injected by an attacker to corrupt the states of the nodes. We propose the use of Set-Valued Observers (SVOs) to detect if the state observations are compatible with the system dynamics for the worst case in a deterministic setting. The concept of Stochastic Set-Valued Observers (SSVOs) is also introduced to construct a set that is guaranteed to contain all possible states with, at least, a pre-specified desired probability. The proposed algorithm is stable in the sense that it requires a finite number of vertices to represent polytopic sets and it allows for the computation of the largest magnitude of the disturbance that an attacker can inject in the network without being detected. Results are presented to reduce the computational cost of this approach and, in particular, by considering only local information and representing the remainder of the network as a disturbance. The case of a consensus algorithm is discussed leading to the conclusion that, by using the proposed SVOs, finite-time consensus is achieved in non-faulty environments. A novel algorithm is proposed that produces less conservative set-valued state estimates by having nodes exchanging local estimates. The algorithm inherits all the previous properties and also enables finite-time consensus computation regardless of the value of the horizon. [ABSTRACT FROM AUTHOR]

Recently, a proportionate-type diffusion LMS algorithm has been proposed by minimizing the mean-square deviation of an intermediate estimate in sparse distributed estimation problems. This algorithm enhances the convergence speed regardless of the sparseness of the vector of interest, but the gain calculation demands huge computational resources. We develop a computationally efficient version of this algorithm that maintains the advantage of the conventional algorithm. We also analyze the mean-square performance of the proposed algorithm without assuming Gaussian distribution of unknown vector estimates (the usual assumption in analysis of proportionate adaptive filters). [ABSTRACT FROM AUTHOR]

In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method. [ABSTRACT FROM AUTHOR]

This paper derives and tests methods to correct regression-based confidence and prediction intervals for groundwater models that neglect sub-parameterization heterogeneity within the hydraulic property fields of the groundwater system. Several levels of knowledge and uncertainty about the system are considered. It is shown by a two-dimensional groundwater flow example that when reliable probabilistic models are available for the property fields, the corrected confidence and prediction intervals are nearly accurate; when the probabilistic models must be suggested from subjective judgment, the corrected confidence intervals are likely to be much more accurate than their uncorrected counterparts; when no probabilistic information is available then conservative bound values can be used to correct the intervals but they are likely to be very wide. The paper also shows how confidence and prediction intervals can be computed and corrected when the weights applied to the data are estimated as part of the regression. It is demonstrated that in this case it cannot be guaranteed that applying the conservative bound values will lead to conservative confidence and prediction intervals. Finally, it is demonstrated by the two-dimensional flow example that the accuracy of the corrected confidence and prediction intervals deteriorates for very large covariance of the log-transmissivity field, and particularly when the weight matrix differs from the inverse total error covariance matrix. It is argued that such deterioration is less likely to happen for three-dimensional groundwater flow systems. [ABSTRACT FROM AUTHOR]

Aim: Pre-diabetes is a state of beta-cell stress caused by excess demand for insulin. Body mass is an important determinant of insulin demand, and BMI has risen substantially over recent time. We sought to model changes in the parameters of glucose control against rising BMI over the past 25years.Methods: Using random coefficient mixed models, we established the correlations between HbA1C, fasting glucose, fasting insulin, HOMA2-IR and BMI in contemporary (2015) children (N=307) at ages 5-16y from the EarlyBird study, and modelled their corresponding values 25years ago according to the distribution of BMI in the UK Growth Standards (1990).Results: There was little change in HbA1C or fasting glucose over the 25y period at any age or in either gender. On the other hand, the estimates for fasting insulin and HOMA2-IR were substantially higher in both genders in 2015 compared with 1990.Conclusion: Insofar as it is determined by body mass, there has been a substantial rise in beta cell demand among children over the past 25years. The change could be detected by fasting insulin and HOMA2-IR, but not by fasting glucose or HbA1C. [ABSTRACT FROM AUTHOR]

This short communication introduces vector fitting for mode estimation from ringdown responses of power systems. The performance of the method is evaluated for different power system models and is found to be very accurate. [ABSTRACT FROM AUTHOR]

This paper presents a new robust adaptive filtering method for transfer alignment by taking into account the systematic errors of observation and kinematic models in the filtering process. The proposed method overcomes the limitation of the traditional Kalman filter, that is, the requirement of precise kinematic and observation models for transfer alignment. It adaptively adjusts and updates the prior information through the equivalent weighting matrix and adaptive factor to resist the disturbances of systematic model errors on system state estimation, thus improving the accuracy of state parameter estimation. Experimental results and comparison analysis demonstrate that the proposed robust adaptive filtering method can effectively improve the performance of transfer alignment, and the achieved performance is much higher than those of the Kalman and traditional robust adaptive Kalman filters. [ABSTRACT FROM AUTHOR]

Dry-plug flow is a variation of two-phase plug flow that occurs in small scale channels and refers to the dry wall conditions at gas portions of the flow regime. Previous experimental studies found a significant increase in pressure drop in this flow regime from the two-phase wet-plug flow regime. In this work an analytical model for the pressure drop of this flow regime is developed and phenomena that influence pressure drop are examined. Unlike previous models, the proposed model seems to be applicable to a wide range of capillary numbers and give good estimations for all static contact angles. Contact angle hysteresis turned out to play a major role in inducing pressure drop in this flow regime. Model’s predictions were in good agreement with previous experimental data. Finally the model is applied to very low capillary number region and pressure drop predictions for this region are presented. [ABSTRACT FROM AUTHOR]

Because of finite stiffness of grinding system, grinding force will cause the tool deflection (the difference between actual cutting depth and nominal cutting depth). During helix path contour grinding, the grinding condition are variable at different grinding point which will bring forward different tool deflection and result in dimensional errors. This paper presents an error analysis model during multi-pass grinding, which can predict the accumulation process of surface profile error induced by tool deflection. It establishes the relationship between profile error and grinding parameters. The estimation method of key model parameters is described in the proposed model through series of experiments. According to the error analysis model, we can implement the varied feed rate and varied cutting depth method for compensating the profile error. The grinding experimentation and compensation grinding verifies the validity of error analysis model and effectiveness of compensation method, and the profile error reduced by 82.1% comparing with grinding process without compensation. [ABSTRACT FROM AUTHOR]

In this paper, a new lattice model is proposed by considering the optimal estimation of flux difference information. The effect of this new consideration upon the stability of traffic flow is examined through linear stability analysis. Furthermore, a modified Korteweg–de Vries (mKdV) equation near the critical point is constructed and solved by means of nonlinear analysis method, and thus the propagation behavior of traffic jam can be described by the kink–antikink soliton solution of the mKdV equation. Numerical simulation is carried out under periodical condition with results in good agreement with theoretical analysis, therefore, it is verified that the new consideration can enhance the stability of traffic systems and suppress the emergence of traffic jams effectively. [ABSTRACT FROM AUTHOR]

Information diffusion has become ubiquitous in various complex networks recently. Despite understanding information diffusion process being of utmost important to optimize network structure and regulate network function, the propagation mechanism of information diffusion is essentially unclear. Here we focus on the evolution prediction of information diffusion process. A problem typically encountered when exploiting traditional diffusion prediction methods is the limitedness of information available on diffusion graph, which hinders them from widespread use. This paper develops a novel prediction method, multi-scale diffusion prediction (MScaleDP). MScaleDP aggregates microscopic spreading modules of individual nodes using a unidirectional label propagation algorithm for macroscopic diffusion prediction, in which the label selection mechanism corresponds to the microscopic spreading decision-making. Through microscopic spreading behavior modeling, the underlying influential factors and the principal driving mechanisms of diffusion process are identified. Moreover, we find that the accuracy of spreading behavior estimation does not always increase with the growth of the feature number. We also find that the accuracy of spreading behavior estimation is not strongly correlated with the estimation model when sufficient features are considered. Our method is successfully tested on microblogging network, and represents a valuable tool for gaining insights on information diffusion process. [ABSTRACT FROM AUTHOR]

Lack of information in occluded regions leads to ambiguity inherent, which is a big challenge for motion estimation. Recently, the sparse model has been widely used since the essential content of the motion field could be effectively preserved with sparse representation. The methods exploiting sparsity acquire representations either directly in the spatio-temporal domain or indirectly in the transform domain. Usually, the sparse model with sparsifying transform is based on patches and thus is more robust agints noise, while the sparse model without sparsifying transform can directly work for an overall image treatment. Aiming at tackling the motion ambiguity efficiently, this paper employs a distinct sparse representation model into a variational framework for estimating occlusion with optical flow. In order to deal with dictionary learning which is computationally expensive and requires a preprocess for extending the sparsifying transform model for arbitrary image sizes, we present a new unified framework to directly generate an overall dictionary via the sparse model without sparsifying transform, and then optimize for small size dictionaries over corresponding patches with the overall dictionary. Our framework is based on the Stein–Weiss analysis function acting as a novel regulariser and a sparsifying transform function respectively in variational and sparsity models. Experiments show that the proposed method outperforms the existing estimation methods of jointing occlusion and optical flow. [ABSTRACT FROM AUTHOR]

Iceberg drift forecasting is challenging. Large uncertainties in iceberg geometry and in iceberg driving forces – current, wind and waves – make accurate forecasts difficult. In this work, a new estimation-forecast scheme is proposed that improves iceberg drift forecasts by using past iceberg drift information to reduce uncertainties. Which of the most uncertain parameters to adjust in order to reduce uncertainties, is guided by simple criteria that are introduced and explained. These criteria are applied to different iceberg drift estimation models and a new parameter estimation process is proposed and implemented in the form of a moving horizon estimator. Different performance indices are introduced to evaluate estimation and forecast performance of the proposed set-up. The set-up is tested in a case study on two iceberg drift trajectories surveyed during a research expedition offshore Newfoundland in 2015. The results indicate that the quality of iceberg drift forecasts improve significantly for short-term forecasts, whereas the improvements are smaller for longer forecasts. Moreover, it is demonstrated that the proposed set-up is superior to solutions based on adjustments of the drag coefficients. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Data imbalance problems arisen from the accumulated amount of data, especially from big data, have become a challenging issue in recent years. In imbalanced data, those minor data sets probably imply much important patterns. Although there are some approaches for discovering class patterns, an emerging issue is that few of them have been applied to cluster minor patterns. In common, the minor samples are submerged in big data, and they are often ignored and misclassified into major patterns without supervision of training set. Since clustering minorities is an uncertain process, in this paper, we employ model selection and evolutionary computation to solve the uncertainty and concealment of the minor data in imbalanced data clustering. Given data set, model selection is to select a model from a set of candidate models. We select probability models as candidate models because they can solve uncertainty effectively and thereby are well-suited to data imbalance. Considering the difficulty of estimating the models' parameters, we employ evolutionary process to adjust and estimate the optimal parameters. Experimental results show that our proposed approach for clustering imbalanced data has the ability of searching and discovering minor patterns, and can also obtain better performances than many other relevant clustering algorithms in several performance indices. [ABSTRACT FROM AUTHOR]

Conventional appearance-based gaze estimation methods employ local or global features as eye gaze appearance descriptor. But these methods don’t work well under natural light with free head movement. To solve this problem, we present an appearance-based gaze estimation method using deep feature representation and feature forest regression. The deep feature is learned through hierarchical extraction of deep Convolutional Neural Network (CNN). And random forest regression with cluster-to-classify node splitting rules is used to take advantage of data distribution in sparse feature space. Experimental results demonstrate that the deep feature has a better performance than local features on calibrated gaze regression. The combination of deep features and random forest regression provides an effective solution for gaze estimation in a natural environment. [ABSTRACT FROM AUTHOR]

The resilient modulus (M R ) of pavement subgrade soils essentially describes the structural response of pavements for a reliable design. Due to the elaborate, expensive and complex experimental estimation of M R factor, several models are proposed for indirect estimation of it which are mostly established based on statistical analyses e.g. regression analyses. The deficiencies of existing models in addition to the complexity of resilient behavior of soils indicate the necessity to develop better models. This study investigates the potential of a powerful hybrid artificial intelligence paradigm, i.e. adaptive neuro-fuzzy inference system (ANFIS), for prediction of M R of flexible pavements subgrade soils. A comprehensive database which comprises a total of 891 experimental datasets conducted on different Ohio cohesive soils is taken from the literature for evolving models. In ANFIS modeling, P #200 , LL, PI, w opt , w c , S r , q u , σ 3 , σ d are considered as input variables and correspondingly the output is M R . Several statistical criteria, validation and verification studies are used for evaluating the performance capability of the obtained model. A sensitivity analysis is utilized to demonstrate the effectiveness of the considered input variables for characterizing M R . Besides, the response of ANFIS based M R model to variations of each input variable is examined using a parametric study and results are compared to those experimentally provided in the literature. Eventually, the obtained results approve the robustness of ANFIS approach for indirect estimation of M R of subgrade soils. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the general decay estimates for the wave equation with acoustic boundary conditions in domains with nonlocally reacting boundary. [ABSTRACT FROM AUTHOR]

Many production systems consist of a sequence of processes or stages. For these systems, relational network DEA can be used and an overall system efficiency (equal to the efficiency of the different processes) can be computed. However, there can be alternative solutions that give different estimations of the process efficiencies and therefore lead to different decompositions of the overall system efficiency. It is not obvious which efficiency decomposition to use. In this paper, it is shown how a Nash bargaining game can be used to compute point estimates of the efficiency of the processes for multistage systems. The proposed approach extends and improves over existing approaches for production systems with just two stages. The rationality principles behind the proposed solution approach are presented and an interesting interpretation of the resulting efficiency decomposition is provided. The fact that this rigorous solution approach leads to such a simple and elegant efficiency decomposition should facilitate its adoption by Expert and Intelligent Systems practitioners. [ABSTRACT FROM AUTHOR]

In this paper, we study the existence of the tangent cone to a positive plurisubharmonic or plurisuperharmonic current with a suitable condition. Some estimates of the growth of the Lelong functions associated with the current and to its d d c are given to ensure the existence of the strict transform of this current. A second proof for the existence of the tangent cone is derived from these estimates. [ABSTRACT FROM AUTHOR]

In this paper, we find bounds for the 1-norm of the inverses of triangular matrices whose columns may decrease to zero. These results complement those obtained by Berenhaut et al. in [1] where the columns cannot decrease to zero. Also, we extend the 1-norm estimates obtained for Toeplitz matrices, by Liu et al. in [5] , to a non-Toeplitz class. Finally, we construct a class of matrices where the estimates obtained are the best. [ABSTRACT FROM AUTHOR]

An accurate knowledge on global solar radiation is particularly required for proper placement and design of solar energy conversion systems. While the meteorological data are measured at most of the weather stations, global solar radiation measurement is not always performed due to high cost of the measurement devices and their operation and maintenance requirements. Therefore, several linear, non-linear and soft computing models are developed to estimate the solar radiation owing to being more economical when compared to installing pyranometers and these models provide satisfactory results. However, it is crucial to choose the most appropriate model for a specific purpose and region. The primary objective of this study is to optimize the performance of the artificial neural network model in order to realize an efficient estimation of solar radiation for Eastern Mediterranean Region of Turkey. Estimation performances are discussed for different structures of neural network by taking into account the number and quality of input features, learning algorithms, number of hidden neurons, correlation between network outputs and targets, and statistical error analysis methods. The presented model indicates that the artificial neural network models illustrate promising in the estimation of monthly mean daily global solar radiation by using commonly available data. In order to indicate the superiority of the performance of the model, it is evaluated with various test years, which are not used for training stage of the model. The presented model provides superior relationship between the estimated and measured values. The test results showed that the coefficient of determination and mean absolute percentage error between the optimized artificial neural network estimations and measured values for testing datasets are higher than 99% and %5, respectively. [ABSTRACT FROM AUTHOR]

In online automotive applications, look-up tables are often used to model nonlinearities in component models that are to be valid over large operating ranges. If the component characteristics change with ageing or wear, these look-up tables must be updated online. Here, a method is presented where a Kalman filter is used to update the entire look-up table based on local estimation at the current operating conditions. The method is based on the idea that the parameter changes observed as a component ages are caused by physical phenomena having effect over a larger part of the operating range that may have been excited. This means that ageing patterns at different operating points are correlated, and these correlations are used to drive a random walk process that models the parameter changes. To demonstrate properties of the method, it is applied to estimate the ohmic resistance of a lithium–ion battery. In simulations the complete look-up table is successfully updated without problems of drift, even in parts of the operating range that are almost never excited. The method is also robust to uncertainties, both in the ageing model and in initial parameter estimates. [ABSTRACT FROM AUTHOR]

The proliferation of papers on energy demand modeling has offered different dimensions both in terms of methodology and estimation. While this development has strengthened our understanding of the subject, it may render the identification of gaps in the literature arduous for future research. Thus, this study sets out to document some emerging issues that seem to have engendered new directions on energy demand modeling ranging from asymmetric price responses, time varying demand parameters, triangulation analyses to seasonal and climate change effects. It is hoped that access to this review complemented with the previous ones will facilitate the comprehension of the extant literature as well as areas of future research in energy demand modeling. [ABSTRACT FROM AUTHOR]