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  1. The focus of this work is on the development of a model for a gantry crane transporting a non point-mass payload such as pipes, where besides the payload swinging, includes the twisting motion also, which can be hazardous if not adequately controlled. Euler-Lagrange equations of motion are derived which permit accounting for payloads whose center of mass does not coincide with the hoisting cable attachment. Work-Energy principle is used to ensure that a collocated Proportional-Derivative (PD)-controller is stabilizing and an input shaper is used to shape the reference profile to permit minimal residual vibration of the payload. 
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  2. The focus of this paper is on the development of velocity constrained time-optimal control profiles for point-to-point motion of a gantry crane system. Assuming that the velocity of the trolley of the crane can be commanded, an optimal control problem is posed to determine the bang-off-bang control profile to transition the system to the terminal states with no residual vibrations. Both undamped and underdamped systems are considered and the variation of the structure of the optimal control profiles as a function of the final displacement is studied and the collapse and birthing of switches in the control profile are explained. To account for uncertainties in model parameters, a robust controller design is posed and the tradeoff of increase in maneuver time to the reduction of residual vibrations is illustrated. 
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  3. Abstract Travelling wave patterns observed in the movement of certain aquatic animals has motivated research in the modification of flow behavior, especially to deal with boundary layer separation in airplane wings. Research has shown that inducing travelling waves on the top surface of the wing can generate sufficient momentum to prevent boundary layer separation without increasing the drag. Due to this effect of propagating waves on the aerodynamics, generation of travelling waves on solid surfaces is being widely studied. Recently, methods such as two-mode excitation, active sink and impedance matching have shown promise in generation of uniform travelling waves in solids with the help of piezo electric actuators. Unfortunately, there are some challenges involved in the experimental application of these methods. Although these techniques have shown to be adequate in laboratory settings, they require laborious tuning procedures which do not guarantee desired trajectories and are followed in light of interference from unwanted modes and their transients. Some methods rely on selective mode excitation, which can cause interference from unwanted modes if the transient behavior of the system is not accounted for. Feed-forward input shaping control methods are proposed that augment the open-loop piezo actuation method (two-mode excitation) and provide a more robust method for generating uniform travelling waves. The input shaping control alters the reference signal such that the parasitic behavior of unnecessary modes is cancelled out. The combination of the mode suppression and selective mode excitation through input shaping is verified experimentally for generation of a smooth travelling waves in finite structures. 
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  4. null (Ed.)
    Global sensitivity analysis aims at quantifying and ranking the relative contribution of all the uncertain inputs of a mathematical model that impact the uncertainty in the output of that model, for any input-output mapping. Motivated by the limitations of the well-established Sobol' indices which are variance-based, there has been an interest in the development of non-moment-based global sensitivity metrics. This paper presents two complementary classes of metrics (one of which is a generalization of an already existing metric in the literature) which are based on the statistical distances between probability distributions rather than statistical moments. To alleviate the large computational cost associated with Monte Carlo sampling of the input-output model to estimate probability distributions, polynomial chaos surrogate models are proposed to be used. The surrogate models in conjunction with sparse quadrature-based rules, such as conjugate unscented transforms, permit efficient calculation of the proposed global sensitivity measures. Three benchmark sensitivity analysis examples are used to illustrate the proposed approach. 
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  5. Abstract

    Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values forroot mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE), andnormalized mean squared error(NRMSE) are$$23.22 \pm 6.39$$23.22±6.39mg/dL, 16.77 ± 4.87 mg/dL,$$12.84 \pm 3.68$$12.84±3.68and$$0.08 \pm 0.01$$0.08±0.01respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of$$80.17 \pm 9.20$$80.17±9.20and$$84.81 \pm 6.11,$$84.81±6.11,respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.

     
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  6. Water resource has become one of the most precious resources in recent decades. Agriculture accounts for about 80\% of the total water usage in US. There is a demanding need for efficient irrigation and water management systems built for sustainable water utilization in smart agriculture. Real time in-situ soil moisture sensing is a vital part for smart agriculture. Traditional electromagnetic (EM) based soil moisture sensing relies on EM based wireless sensor or ground penetrating radar (GPR) system. Based on the receiving signal strength and delay, tomographic techniques are used to derive the dielectric parameters of the soil, which are then into soil moisture distribution using empirical model. However, the EM signal attenuate sharply during underground propagation because of high operating frequency and lossy medium. In order to counter the disadvantage for underground sensing, we propose a Magnetic Induction (MI) based large range soil moisture sensing scheme in inhomogeneous environments. Here, we present the topology of the sensing system and analyze the channel model. The sensing process is based on transformed model, the conductivity and permittivity distribution are derived using SIRT algorithm. Through COMSOL simulation and analytical results, our proposed soil moisture sensing method achieves a root mean square error (RMSE) of 0.06 m^3/m^3 in 40 m 2D scale inhomogeneous environment range. 
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  7. The objective of this paper is to develop an open loop insulin input profile over a span of 24 hours which makes the glucose trajectory of a Type 1 diabetic person track a target glucose trajectory. The Bergman minimal model is chosen to represent the glucose-insulin dynamics which is shown to be differentially flat. An optimal control problem is posed by parameterizing the differentially flat output of the Bergman model using Fourier series, to result in an input profile that can be repeatedly administered every day. The solution to the optimization problem is then shown to present acceptable performance in terms of tracking and adhering to imposed constraints. 
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  8. In this paper, a novel way to compute derivativebased global sensitivity measures is presented. Conjugate Unscented Transform (CUT) is used to evaluate the multidimensional definite integrals which lead to the sensitivity measures. The method is compared with Monte Carlo estimates as well as the screening method of Morris. It is shown that using CUT provides a much more accurate estimate of sensitivity measures as compared to Monte Carlo (with far lesser computational cost) as well as the Morris method (with similar computational cost). Illustrations on three test functions are presented as evidence. 
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