Nonlinear Optimization Techniques Research Articles

Road Damage Detection Based on Unsupervised Disparity Map Segmentation

This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed disparity map is then segmented using Otus's thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%.

On Minimisation of Earthing System Touch Voltages

Finding cost efficient earthing system design with acceptable level of safety might be quite tedious work. Thus, many earthing system engineers try to find the most suitable design either by employing only their best experience or taking advantage of some more complex optimisation programs. Although both approaches might work well under certain circumstances, they might fail either due to counter-intuitiveness of the specific situation or by misunderstanding of the applied optimisation method, its limitations etc. Thus, in this paper, the earthing system design optimisation problem was addressed by analysing optimisation simulation results together with conducted sensitivity analysis. In the paper, a simple double ring earthing system was optimised while using five different optimisation methods. The earthing system was placed in different horizontally stratified soil models and the earthing system was optimised by minimising touch voltages instead of commonly minimised earth potential rise. The earthing system was modelled by Ansys Maxwell software. Apart from using Ansys Maxwell built-in optimisers, the possible solution space has also been mapped by performing sensitivity analysis with changing the earthing system design dimensions and the results of optimisation were compared and validated. It was found out that the Sequential Non-Linear Programming Optimisation technique was quite superior to the other techniques. Additionally, in most cases, the Ansys Maxwell optimiser was able to found optimal solution; however, in some cases, based on the initial conditions, it might get stuck in local minima or the results might be influenced by the solution noise. Additionally, some quite non intuitive dependencies of earthing system electrodes positions had been found when different spatial dimensions constraints are used.

Design of on-board calibration methods for a digital sun sensor based on Levenberg–Marquardt algorithm and Kalman filters

Digital sun sensor is one of the most important sensors used in the Attitude Determination System (ADS) of the satellite. Due to the harsh environmental conditions that exist in the space, various distortions may occur in the sun sensor optical system that lead to the reduced accuracy of this equipment. So, it is necessary to recalibrate the optical parameters of the aforementioned sensors. For this purpose, first a novel attitude independent error model is proposed for the SS-411 sun sensor that includes the central point of the CCD array, installation error, filter thickness and sensor misalignment. So, the mutual interfaces between the sensor parameters are considered in the developed model. In order to extract the sensor parameters, a nonlinear optimization technique called the Levenberg–Marquardt is applied to the developed model as a batch algorithm. In addition, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) have been utilized as sequential strategies. It will be shown that by considering a worst case of variation amount for sensor parameters, an accuracy improvement of about 17° is achieved by the developed calibration algorithms. Comparison between the developed algorithms represents that UKF has higher accuracy, shorter time convergence but higher computational load.

On the Optimal Two-Antenna Static Beamforming With Per-Antenna Power Constraints

This letter provides a theoretical analysis for the static beamforming problem when the base station is equipped with two antennas. It is assumed that the base station cannot change the beamforming vector dynamically due to hardware restrictions. The objective is to find the static beamforming vector that maximizes network utility when long-term information about the user location is available. Non-linear optimization techniques are used to find the solution of the problem for two different network utility functions: average SNR and average rate. Numerical results are provided to support the analysis.

On the Lateral Instability Analysis of MEMS Comb-Drive Electrostatic Transducers.

This paper investigates the lateral pull-in effect of an in-plane overlap-varying transducer. The instability is induced by the translational and rotational displacements. Based on the principle of virtual work, the equilibrium conditions of force and moment in lateral directions are derived. The analytical solutions of the critical voltage, at which the pull-in phenomenon occurs, are developed when considering only the translational stiffness or only the rotational stiffness of the mechanical spring. The critical voltage in a general case is numerically determined by using nonlinear optimization techniques, taking into account the combined effect of translation and rotation. The influences of possible translational offsets and angular deviations to the critical voltage are modeled and numerically analyzed. The investigation is then expanded for the first time to anti-phase operation mode and Bennet’s doubler configuration of the two transducers.

DISPERSE: A Decentralized Architecture for Content Replication Resilient to Node Failures

This paper introduces DISPERSE, a distributed scalable architecture for delivery of content and services that provides resilience against node failure through location-independent storage and replication of content. Current content delivery networks (CDNs) have, at least to some degree, a centralized structure thus susceptible to a single point of failure. DISPERSE addresses this limitation by implementing a fully de-centralized structure. DISPERSE is a two-layer architecture: the first layer (front-end layer) exposes services (e.g., Web, SFTP) to clients; the second layer (back-end layer) provides reliable distributed storage of content and application state. Content in DISPERSE's back-end layer is stored and exchanged as Named Data Network (NDN) content objects. This allows DISPERSE to implement fine-grained, location-independent, fully decentralized content replication mechanisms. We validate the performance of DISPERSE under two node failure scenarios. In the first scenario, content can be stored in any DISPERSE node, and all nodes are equally likely to fail. In this scenario, we use non-linear optimization techniques to determine the optimal number of content copies under availability and latency constraints. In the second scenario, different nodes fail with different probabilities, and content is stored in nodes according to its value, node failure probability, and resource availability. This scenario is addressed as an instance of the minimum cost flow problem. Our results show that DISPERSE reduces the failure of content retrieval by five orders of magnitude compared to common CDN implementations, without significantly increasing content retrieval delay. Further, numerical results show that DISPERSE improves content availability by a factor of 1.3× - 2.3× when deploying the minimum cost flow algorithm.

Finite Horizon Optimal Nonlinear Spacecraft Attitude Control

This research is devoted to present spacecraft attitude control via a finite-horizon nonlinear optimal control technique. The spacecraft kinematics are represented using the modified Rodrigues parameters, which possess singularities for eigenaxis rotations greater than 180 degree. The proposed technique is based on State Dependent Riccati Equation (SDRE). In this technique, the differential Riccati equation is converted to a linear Lyapunov differential equation. This technique can be applied for finite-horizon nonlinear regulation and tracking problems. The proposed technique is effective for a wide range of operating points. Simulation results are given to illustrate the effectiveness of the finite horizon technique.

Indirect robust suboptimal control of two-satellite electromagnetic formation reconfiguration with geomagnetic effect

As a novel approach to control the relative motion of a satellite formation, electromagnetic formation flight (EMFF) has some prominent advantages, such as no propellant consumption and no plume contamination, and has a broad prospect of application in such fields as on-orbit detection and optical interferometry. The current paper investigates the optimal control for the reconfiguration of a two-satellite electromagnetic formation using the nonlinear quadratic optimal control technique. Specifically, the effects of the Earth’s magnetic field on the EMFF satellites are analyzed, and then the nonlinear translational dynamic model of a two-satellite electromagnetic formation is derived by utilizing the analytical mechanics theory. Considering the high nonlinearity and coupling in the dynamic model and the actuator saturation, a closed-loop robust suboptimal control strategy based on the indirect robust control scheme and the θ-D technique is proposed with robust stability and optimality. To ensure a further reduction of control input, the designed suboptimal controller is modified by applying the Tracking-Differentiator. The feasibility of the derived translational dynamics and proposed control strategy for the robust reconfiguration mission is validated through theoretical analysis and numerical simulations.

Memory Based Hybrid Dragonfly Algorithm (MHDA): a New Technique for Determining Model Parameter in Vertical Electrical Sounding (VES) Data

Vertical Electrical Sounding (VES) data inversion is a nonlinear inversion problem because several models can fit to the observed data. Therefore, a new approach based on nonlinear optimization technique is implemented which is called Memory based Hybrid Dragonfly Algorithm (MHDA). It is proposed to solve drawback of Dragonfly Algorithm (DA), i.e. low convergence rate which is caused by high exploration behaviour of DA. The drawback can lead to the local optimum solutions. MHDA successfully balances exploration and exploitation behaviours of DA to obtain global optimum solution. In this research, initially, MHDA is tested for the noise contaminated synthetic VES data to assess its performance. Subsequently, MHDA is applied for the field VES data. In both results, MHDA is able to provide Posterior Distribution Model (PDM) which is obtained from exploration process. All accepted models of PDM have lower misfit value than specified tolerance value in the inversion process. The PDM can be used to estimate solution via median value of PDM. Additionally, the uncertainty estimation of obtained solution can be determined from standard deviation value of PDM. The inversion results of synthetic and field VES data indicate that MHDA is an innovative technique to solve VES data inversion problem.

Visual Closed-Loop Dynamic Model Identification of Parallel Robots Based on Optical CMM Sensor

Parallel robots present outstanding advantages compared with their serial counterparts; they have both a higher force-to-weight ratio and better stiffness. However, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. This paper focuses on the dynamic model identification of the 6-DOF parallel robots for advanced model-based visual servoing control design purposes. A visual closed-loop output-error identification method based on an optical coordinate-measuring-machine (CMM) sensor for parallel robots is proposed. The main advantage, compared with the conventional identification method, is that the joint torque measurement and the exact knowledge of the built-in robot controllers are not needed. The time-consuming forward kinematics calculation, which is employed in the conventional identification method of the parallel robot, can be avoided due to the adoption of optical CMM sensor for real time pose estimation. A case study on a 6-DOF RSS parallel robot is carried out in this paper. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, a visual closed-loop output-error identification method is proposed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

Optimal valve closure operations for pressure suppression in fluid transport pipelines

When a valve is suddenly closed in fluid transport pipelines, a pressure surge or shock is created along the pipeline due to the momentum change. This phenomenon, called hydraulic shock, can cause major damage to the pipelines. In this paper, we introduce a hyperbolic partial differential equation &#x0028 PDE &#x0029 system to describe the fluid flow in the pipeline and propose an optimal boundary control problem for pressure suppression during the valve closure. The boundary control in this system is related to the valve actuation located at the pipeline terminus through a valve closing model. To solve this optimal boundary control problem, we use the method of lines and orthogonal collocation to obtain a spatial-temporal discretization model based on the original pipeline transmission PDE system. Then, the optimal boundary control problem is reduced to a nonlinear programming &#x0028 NLP &#x0029 problem that can be solved using nonlinear optimization techniques such as sequential quadratic programming &#x0028 SQP &#x0029. Finally, we conclude the paper with simulation results demonstrating that the full parameterization &#x0028 FP &#x0029 method eliminates pressure shock effectively and costs less computation time compared with the control vector parameterization &#x0028 CVP &#x0029 method.

Integrated Dynamic Characterization of Thermorheologically Simple Viscoelastic Materials Accounting for Frequency, Temperature, and Preload Effects.

In vibration insulation projects, a parameter affecting the dynamic properties of the viscoelastic materials is the previous static load acting on the supports, denominated here as the ‘preload’. Most of the currently-used methodologies obtain the dynamic properties by considering only the effects of temperature and frequency. The additional effect of preload can be added to the usual methodologies by employing the hyperelastic theory developed by Mooney–Rivlin. The current work proposes an integrated approach to characterize thermorheologically simple viscoelastic materials, including the preload effect along with the influence of temperature and frequency. The proposed method uses a hybrid optimization process, combining a genetic algorithm (GA) and a non-linear optimization technique—named ‘simplex’—in an inverse problem structure applied to all experimental data at hand. A set of samples of elastomer BT-806 55 (butyl rubber) was tested at various temperatures, frequencies, and preloads. The comparison between the results of the present methodology and traditional approaches to a variation in the dynamic properties at all frequencies and temperatures for a constant vibration amplitude. The present results prove that the proposed methodology is a viable alternative to represent the dynamic properties of materials used in vibration isolation.

Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data

The time-series synthetic aperture radar (SAR) and optical satellite data were used for the leaf area index (LAI) estimation of wheat crop using modified water cloud model (MWCM) in Varanasi district, India. In this study, MWCM was developed by including scale invariant vegetation fraction (fveg) in the old WCM for the estimation of LAI. The non-linear least square optimization technique was applied to determine the optimum model parameters for the retrieval of LAI which was further validated with the observed LAI. The estimated values of LAI by MWCM at VV polarization shows good correspondence (R2 = 0.901 and RMSE = 0.456 m2/m2) with the observed LAI values than at VH polarization (R2 = 0.742 and RMSE = 0.521 m2/m2).The MWCM shows great potential for the LAI estimation of wheat crop by incorporating optical data (i.e. Sentinel-2) in terms of fveg with SAR data (i.e. Sentinel-1A).

Solubility measurements and thermodynamic modeling of pyrazinamide in five different solvent-antisolvent mixtures

Pyrazinamide, an important first line drug for the treatment of Mycobacterium tuberculosis, has different polymorphic forms among which the plate like metastable δ-polymorphic form is desirable for ease of downstream processing. Pyrazinamide has reasonably good solubility in acetone, 1,4-dioxane and methanol; but very low solubility in toluene and cyclohexane. Also, the δ-polymorphic form of pyrazinamide can be obtained by antisolvent crystallization using any of these five solvent-antisolvent pairs. In this work, the solubility of pyrazinamide in acetone-toluene, acetone-cyclohexane, 1,4-dioxane-toluene, 1,4-dioxane-cyclohexane and methanol-toluene mixtures is measured by gravimetric method within the temperature range of 283.15 K to 333.15 K over antisolvent composition upto 70 wt%. The solubility of pyrazinamide increases with increase in temperature for all systems at constant antisolvent (toluene and cyclohexane) composition and decreases with increase in antisolvent composition for the given temperature range for all systems except methanol-toluene system. The solubility of pyrazinamide in methanol-toluene system increases with increase in toluene composition upto 40 wt% and then decreases with increase in toluene composition. Three thermodynamic models namely UNIQUAC, NRTL, and Wilson are used to correlate the solubility of pyrazinamide in above solvent-antisolvent mixtures. A nonlinear optimization technique was used to determine the adjustable parameters of various models. The accuracy of the model correlation has been evaluated on the basis of relative average deviation, root mean square deviation and Akaike Information Criterion. All the models represented the experimental solubility data with good accuracy, the maximum root mean square deviation being 5.21. The dissolution enthalpy and dissolution entropy of pyrazinamide in above solution mixtures were calculated from the slope and intercept of van't Hoff equation which shows that solvation of pyrazinamide in these mixed solvents is endothermic and enthalpy-driven.

Термодинамическое проектирование семейства малоразмерных газотурбинных двигателей

The paper presents a procedure for selecting work cycle parameters and describes thermodynamic design of a small-scale gas turbine engine family consisting of a small-scale gas turbine engine and a gas turbine plant comprising a free turbine driving a power generator, on the basis of a standardized gas generator. In order to select reasonable work cycle parameter values for the small-scale gas turbine engine and gas turbine plant we used a non-linear optimisation technique accounting for functional and parametric constraints as implemented in the ASTRA CAE software. Calculation results allowed us to plot the locally optimal work cycle parameter regions for the small-scale gas turbine engine and gas turbine plant according to the efficiency criteria for both engines, which are specific fuel consumption and net energy conversion efficiency. Taking the constraints into account, we selected reasonable values for the standardized gas generator parameters within the compromise region obtained, specifically the turbine inlet temperature and compressor pressure ratio. Our quantitative results show how the efficiency indices decline in the engine family featuring a standardized gas generator as compared to engines equipped with individually tailored gas generators. Designing a standardized gas generator in advance makes it possible to decrease engine development costs and time, ensure a higher reliability and a lower cost of production.

Controller synthesis for negative imaginary systems using nonlinear optimisation and H 2 performance measure

ABSTRACT Negative imaginary (NI) systems theory has attracted considerable attention in the area of robust control of highly resonant flexible structures systems and nano-positioning systems, where the NI property is naturally satisfied. In this paper, we present a control synthesis methodology for NI systems based on nonlinear optimisation techniques. In the presented method, a parametrised library of strictly negative imaginary (SNI) controllers is created and used in a standard numerical nonlinear optimisation setup. The SNI controller library contains generic controllers that are widely used in the control highly resonant flexible structures such as positive position feedback (PPF) and integral resonant control (IRC). Sequential quadratic programming (SQP) techniques are used in the numerical optimisation problem. The synthesised controller satisfies the SNI property as well as optimising performance. As an application of these results, an example of controlling a Euler–Bernoulli beam with piezoelectric actuator and sensor is presented.

Voltage stability constrained line‐wise optimal power flow

The purpose of optimal power flow (OPF) is to optimise an objective function subject to a set of operating constraints. It remains an active research area because of using the bus-wise power balance equations, which leads to a non-linear solution space, resulting in OPF yielding local optimal solutions, thereby causing significant economic loss. In this study, first, a new line-wise OPF (LWOPF) formulation is proposed. Thereafter, a maximum loadability factor, as a voltage collapse indicator, is derived and combined with LWOPF constraints to form a voltage stability constrained LWOPF (VSCLWOPF) model. As the line-wise power balance equations are based upon the square of voltage magnitudes, it results in significant improvement in the solution space and lower-order terms in all computational steps. The LWOPF and VSCLWOPF formulations, are solved using non-linear optimisation technique, tested on several benchmark and real power systems. Results show that the proposed LWOPF is accurate, provides monotonic convergence, and scales well for large systems. It provides a better solution and is consistently faster, up to twice the speed of MATPOWER, due to reduced computational needs. Results of VSCLWOPF show that, for the same voltage stability level, the solution costs less than that obtained by classical bus-wise OPF.

A comprehensive study of an economic order quantity model under fuzzy monsoon demand

This article deals with a classical economic order quantity (EOQ) model under monsoon type fuzzy demand rate. It is nothing but the generalization of cloudy fuzzy model. We split the EOQ model into three parts according to the real-time fuzzy components of the demand rate. To defuzzify the model we develop an algorithm and the solution is obtained with the help of a nonlinear optimization technique that requires maximum aspiration level of the fuzzy membership of the objective function. Moreover, for comparative study we take numerical results of the crisp, general fuzzy and cloudy fuzzy model also. By this study we have shown that the decision maker might have to choose the monsoon type fuzzy environment all the time to control the proposed inventory. Moreover, sensitivity analysis and graphical illustrations are made to justify the new fuzzy approach.

Water treatment residual-coated wood mulch for addressing urban stormwater pollution.

Innovative treatment materials and technologies are demanded to address urban stormwater pollutants that challenge traditional infrastructure. This study aimed to investigate adsorption behaviors of aluminum-based water treatment residual (WTR)-coated mulch for capturing representative runoff pollutants (i.e., P, Cu, Zn, and Pb) and evaluate its treatment performance in a filtration bed. Data from batch studies were fit using the nonlinear least square optimization technique. Adsorption kinetic data followed the pseudo-2nd -order reaction patterns, while the adsorption isotherm data obeyed the Freundlich models. Model fitting passed the chi-square tests, as a statistical goodness-of-fit criterion, at a 90% confidence level. Column studies indicate that the WTR-coated mulch with a bed depth of 5.1 or 10.2cm could effectively alleviate flow-weighted mean concentrations of these pollutants, with a minimal aluminum release, during treatment of the equivalent annual runoff in a typical U.S. Northeastern catchment. This study demonstrates that WTR-coated mulch is an effective and safe adsorbent media to tackle urban stormwater pollution. PRACTITIONER POINTS: Aluminum-based WTR-coated wood mulch can simultaneously and effectively capture representative metals and phosphate in urban runoff. The pollutant adsorption follows the pseudo-2nd -order kinetic reaction patterns and the Freundlich isotherm model. WTR-coated mulch (5.1-10.2cm bed depth) sufficiently treats the runoff generated annually in a typical U.S. Northeastern catchment. Higher and more reliable pollutant removals can be achieved with a greater bed depth of the coated mulch in a filtration bed. Aluminium release is minimal during application of the WTR-coated wood mulch.

Unscented MPSP for Optimal Control of a Class of Uncertain Nonlinear Dynamic Systems

A new computationally efficient nonlinear optimal control synthesis technique, named as unscented model predictive static programming (U-MPSP), is presented in this paper that is applicable to a class of problems with uncertainties in time-invariant system parameters and/or initial conditions. This new technique is a fusion of two recent ideas, namely MPSP and Riemann–Stieltjes optimal control problems. First, unscented transform is utilized to construct a low-dimensional finite number of deterministic problems. The philosophy of MPSP is utilized next so that the solution can be obtained in a computational efficient manner. The control solution not only ensures that the terminal constraint is met accurately with respect to the mean value, but it also ensures that the associated covariance matrix (i.e., the error ball) is minimized. Significance of U-MPSP has been demonstrated by successfully solving two benchmark problems, namely the Zermelo problem and inverted pendulum problem, which contain parametric and initial condition uncertainties.