Robust design of sliding mode control for airship trajectory tracking with uncertainty and disturbance estimation

The robotic airship can provide a promising aerostatic platform for many potential applications.These applications require a precise autonomous trajectory tracking control for airship.Airship has a nonlinear and uncertain dynamics.It is prone to wind disturbances that offer a challenge for a trajectory tracking control design.This paper addresses the airship trajectory tracking problem having time varying reference path.A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters.It uses extended Kalman filter(EKF)for uncertainty and disturbance estimation.The estimated parameters are used by sliding mode controller(SMC)for ultimate control of airship trajectory tracking.This comprehensive algorithm,EKF based SMC(ESMC),is used as a robust solution to track airship trajectory.The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies.The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis.The simulation results show that the proposed method efficiently tracks the desired trajectory.The method solves the stability,convergence,and chattering problem of SMC under model uncertainties and wind disturbances.

Airship aerodynamic model estimation using unscented Kalman filter

An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.

Delineation of an Urban Community Life Circle Based on a Machine-Learning Estimation of Spatiotemporal Behavioral Demand

Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

Ambiguity Resolution in Direction of Arrival Estimation with Linear Antenna Arrays Using Differential Geometry

Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities among the angles of targets,which may result inmisinterpretation of such targets.In order to cope up with such ambiguities,various techniques have been proposed.Unfortunately,none of them fully resolved such a problem because of rank deficiency and high computational cost.We aimed to resolve such a problem by proposing an algorithm using differential geometry.The proposed algorithm uses a specially designed doublet antenna array,which is made up of two individual linear arrays.Two angle observation models,ambiguous observation model(AOM)and estimated observation model(EOM),are derived for each individual array.The ambiguous set of angles is contained in the AOM,which is obtained from the corresponding array elements using differential geometry.The EOM for each array,on the other hand,contains estimated angles of all sources impinging signals on each array,as calculated by a direction-finding algorithm such as the genetic algorithm.The algorithm then contrasts the EOM of each array with its AOM,selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM.In comparison to existing techniques,the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection,resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios.The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.

Letter to the Editor Re “Fractional Modeling and SOC Estimation of Lithium-ion Battery”

A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.

落叶果树需冷量及其估算模型研究进展

落叶果树的需冷量得到满足,才能顺利完成自然休眠,进行下一个生长发育循环(尤其是正常开花结果),否则其他环境条件再适宜,果树也不萌芽开花,即使萌芽也不整齐,生长结果不良,达不到促成栽培的目的[1].因此,对落叶果树需冷量及其估算的研究至关重要,成为落叶果树设施栽培成败的关键.本文仅就需冷量估算模型进行介绍,以期为设施栽培和更完善的需冷量估算提供理论依据.……

Hot spot temperature prediction and operating parameter estimation of racks in data center using machine learning algorithms based on simulation data

In this paper,models to predict hot spot temperature and to estimate cooling air’s working parameters of racks in data centers were established using machine learning algorithms based on simulation data.First,simulation models of typical racks were established in computational fluid dynamics(CFD).The model was validated with field test results and results in literature,error of which was less than 3%.Then,the CFD model was used to simulate thermal environments of a typical rack considering different factors,such as servers’power,which is from 3.3 kW to 20.1 kW,cooling air’s inlet velocity,which is from 1.0 m/s to 3.0 m/s,and cooling air’s inlet temperature,which is from 16℃ to 26℃ The highest temperature in the rack,also called hot spot temperature,was selected for each case.Next,a prediction model of hot spot temperature was built using machine learning algorithms,with servers’power,cooling air’s inlet velocity and cooling air’s inlet temperature as inputs,and the hot spot temperatures as outputs.Finally,based on the prediction model,an operating parameters estimation model was established to recommend cooling air’s inlet temperatures and velocities,which can not only keep the hot spot temperature at the safety value,but are also energy saving.

Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data

Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.

Meso-scale Discovery Assay Detects the Changes of Plasma Cytokine Levels in Mice after Low or High LET Ionizing Irradiation

Objective To obtain precise data on the changes in the levels of 29 cytokines in mice after high or low linear energy transfer(LET)irradiation and to develop an accurate model of radiation exposure based on the cytokine levels after irradiation.Methods Plasma samples harvested from mice at different time points after carbon-ion or X-ray irradiation were analyzed using meso-scale discovery(MSD),a high-throughput and sensitive electrochemiluminescence measurement technique.Dose estimation equations were set up using multiple linear regression analysis.Results The relative levels of IL-6 at 1 h,IL-5 and IL-6 at 24 h,and IL-5,IL-6 and IL-15 at 7 d after irradiation with two intensities increased dose-dependently.The minimum measured levels of IL-5,IL-6 and IL-15 were up to 4.0076 pg/mL,16.4538 pg/mL and 0.4150 pg/mL,respectively.In addition,dose estimation models were established and verified.Conclusions The MSD assay can provide more accurate data regarding the changes in the levels of the cytokines IL-5,IL-6 and IL-15.These cytokines could meet the essential criteria for radiosensitive biomarkers and can be used as radiation indicators.Our prediction models can conveniently and accurately estimate the exposure dose in irradiated organism.

Partially Overlapping Channel Assignment Using Bonded and Non-Bonded Channels in IEEE 802.11n WLAN

Nowadays,wireless local area network(WLAN)has become prevalent Internet access due to its low-cost gadgets,flexible coverage and hasslefree simple wireless installation.WLAN facilitates wireless Internet services to users with mobile devices like smart phones,tablets,and laptops through deployment of multiple access points(APs)in a network field.Every AP operates on a frequency band called channel.Popular wireless standard such as IEEE 802.11n has a limited number of channels where frequency spectrum of adjacent channels overlaps partially with each other.In a crowded environment,users may experience poor Internet services due to channel collision i.e.,interference from surrounding APs that affects the performance of the WLAN system.Therefore,it becomes a challenge to maintain expected performance in a crowded environment.A mathematical model of throughput considering interferences from surrounding APs can play an important role to set up a WLAN system properly.While set up,assignment of channels considering interference can maximize network performance.In this paper,we investigate the signal propagation of APs under interference of partially overlapping channels for both bonded and non-bonded channels.Then,a throughput estimation model is proposed using difference of operating channels and received signal strength indicator(RSSI).Then,a channel assignment algorithm is introduced using proposed throughput estimation model.Finally,the efficiency of the proposal is verified by numerical experiments using simulator.The results show that the proposal selects the best channel combination of bonded and non-bonded channels that maximize the performance.

Model Reduction by Generalized Falk Method for Efficient Dedicated to Professor Karl Stark Pister for his 95th birthday Field-Circuit Simulations

The Generalized Falk Method(GFM)for coordinate transformation,together with two model-reduction strategies based on this method,are presented for efficient coupled field-circuit simulations.Each model-reduction strategy is based on a decision to retain specific linearly-independent vectors,called trial vectors,to construct a vector basis for coordinate transformation.The reduced-order models are guaranteed to be stable and passive since the GFM is a congruence transformation of originally symmetric positive definite systems.We also show that,unlike the Pade-via-Lanczos(PVL)method,the GFM does not generate unstable positive poles while reducing the order´of circuit problems.Further,the proposed GFM is also faster when compared to methods of the type Lanczos(or Krylov)that are already widely used in circuit simulations for electrothermal and electromagnetic problems.The concept of response participation factors is introduced for the selection of the trial vectors in the proposed model-reduction methods.Further,we present methods to develop simple equivalent circuit networks for the field component of the overall field-circuit system.The implementation of these equivalent circuit networks in circuit simulators is discussed.With the proposed model-reduction strategies,significant improvement on the efficiency of the generalized Falk method is illustrated for coupled field-circuit problems.

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

Imposing temporal consistency on deep monocular body shape and pose estimation

Accurate and temporally consistent modeling of human bodies is essential for a wide range of applications,including character animation,understanding human social behavior,and AR/VR interfaces.Capturing human motion accurately from a monocular image sequence remains challenging;modeling quality is strongly influenced by temporal consistency of the captured body motion.Our work presents an elegant solution to integrating temporal constraints during fitting.This increases both temporal consistency and robustness during optimization.In detail,we derive parameters of a sequence of body models,representing shape and motion of a person.We optimize these parameters over the complete image sequence,fitting a single consistent body shape while imposing temporal consistency on the body motion,assuming body joint trajectories to be linear over short time.Our approach enables the derivation of realistic 3D body models from image sequences,including jaw pose,facial expression,and articulated hands.Our experiments show that our approach accurately estimates body shape and motion,even for challenging movements and poses.Further,we apply it to the particular application of sign language analysis,where accurate and temporally consistent motion modelling is essential,and show that the approach is well-suited to this kind of application.

Study on Ground Deformation during Shield Tunnel Construction

Through the systematic analysis of the ground settlement generated by the process of shield tunneling,the relationships between ground deformation and construction parameters are studied in this paper.Based on the assumption of linear small deformation,a mathematical model of the relationship between ground deformation and construction parameters is set up.The principle and method of optimization for estimating ground deformation is studied.The actual measured data are compared with the results of theoretical analysis in a case.Considering different ground formations in different construction sites with different adverse effects on surface and underground structures,the ground surface deformations caused by shield tunneling is an aimed topic in this paper.The contributions and research implications are the revealed relationships between the ground deformation and the shield tunneling parameters during construction.

Partial Linear Model Averaging Prediction for Longitudinal Data

Prediction plays an important role in data analysis.Model averaging method generally provides better prediction than using any of its components.Even though model averaging has been extensively investigated under independent errors,few authors have considered model averaging for semiparametric models with correlated errors.In this paper,the authors offer an optimal model averaging method to improve the prediction in partially linear model for longitudinal data.The model averaging weights are obtained by minimizing criterion,which is an unbiased estimator of the expected in-sample squared error loss plus a constant.Asymptotic properties,including asymptotic optimality and consistency of averaging weights,are established under two scenarios:(i)All candidate models are misspecified;(ii)Correct models are available in the candidate set.Simulation studies and an empirical example show that the promise of the proposed procedure over other competitive methods.

Wearable sensors for 3D upper limb motion modeling and ubiquitous estimation

Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health care, and navigation. Because of the agility, upper limb motion estimation is the most difficult problem in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and then estimate their movements separately; therefore, the estimated motion are always with serious distortion. In this paper, we propose a novel ubiquitous upper limb motion estimation method using wearable microsensors, which concentrates on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure as a link structure with 5 degrees of freedom is firstly proposed to model human upper limb motion. After that, parameters are defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb are derived, and an unscented Kalman filter is invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.

Linking observation,modelling and satellite-based estimation of global land evapotranspiration

Evapotranspiration(ET)is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance.In this paper,the exited actual ETmain datasets in global scale,and the global ET modeling and estimates were focused on discussion.The Source energy balance(SEB)models,empirical models and other process-based models are summarized.Accuracy for ET estimates by SEBmodels highly depends on accurate surface temperature retrieval,and SEB models are hard to apply in large heterogeneous surface.The Penman-Monteith(PM)equations are thought to be with considerable sound mechanism.However,it involves large number of parameters,which are not all global available.A simplified PM equation by Priestley and Taylor(PT)is found to perform well on well-watered surface.For both PM and PT equations in estimating ET,the key is to consider the constraint from surface resistance primarily water stress.Empiricalmodels are simple but the accuracy of which highly depends on training samples.Coupling satellite data into ET models can improve ET estimates with higher resolution spatiotemporal information inputs;However,finding the most proper way to estimate global ET remains problematic.Several reasons for this issue are also analyzed in this review.

Improved estimation model and empirical analysis of relationship between agricultural mechanization level and labor demand

The research on the relationship between mechanization level in planting industry and labor demand was carried out based on the present literatures.The former estimation model of labor demand in planting industry was established without analyzing the effects of planting structure on labor demand in planting industry.The purpose of the research is to develop and perfect the theory of estimating both labor demand and rural surplus labor in planting industry,then to provide some theoretical references for scientific estimation.The model established in this research can be used to calculate not only the amount of current labor demand,but also the demand in the various moment of future according to forecasted mechanization level and cultivated areas.Furthermore,it was explored how to obtain the indexes of cultivated areas,mechanization level and the average cultivated area that each labor can burden when the mechanization level is 0 and 100%.According to statistics principle,the methods of inspection to eliminate abnormal data and data processing were given in order to make the data more credible.Finally,an example was presented for demonstration purposes.