Nonlinear Optimization Techniques Research Articles

An Automated Technique for Optic Disc Detection in Retinal Fundus Images Using Elephant Herding Optimization Algorithm

Background: Glaucoma and diabetic retinopathy are known to be the prime reasons for causing irrevocable blindness in the world. However, the complete vision loss can be obstructed through regular screening of the eye to detect the disorder at an early stage. Objective: In this paper, we have presented a novel nonlinear optimization technique that helps in automatic detection of the optic disc boundary from retinal fundus images thereby satisfying the anatomical constraints by using elephant herding optimization algorithm. Methods: In our approach, median filter has been used for the noise removal in retinal images. The pre-processed image is passed to the metaheuristic algorithm known as Elephant Herding Optimization algorithm. Results: The proposed technique for optic disc segmentation has been applied and tested on four standard publicly available datasets namely DRIVE, DIARETDB1, STARE and DRIONS-DB. The ground truth of the optic disc boundary has been collected from two experts of glaucoma: Expert A and Expert B who are specialists of glaucoma. The quantitative and qualitative analysis has been done to evaluate the performance of optic disc segmentation techniques. Conclusion: The proposed technique for optic disc detection helps to obtain the smooth boundaries in retinal fundus images. The aim has been successfully achieved by proposing an approach using EHO to achieve optimized solution. The effectiveness of the approach has been evaluated on for benchmark datasets and the acquired results have shown the accuracy values to be 100% for DRIVE, 100% for DIARETDB1, 99.25% for STARE and 99.99% for DRIONS-DB.

Simulation of geoelectrical responses of structures buried in freshwater and brackish water environments

Marine geoelectrical simulation offers the opportunity to predict matrix type, interstitial fluid and geometry of buried structures. In this research, simulation of geoelectrical responses over buried structures in freshwater and brackish water environments were tested. A model tank made of acrylic and housed within an iron frame was built for the experiment. The tank was filled with water and sieved sediment having grain sizes ≤1.18 mm, corresponding to very coarse sand to colloid. Four models were designed namely freshwater static model (FSM), brackish water static model (BSM), freshwater model with buried structures (FMBS), and brackish water model with buried structures (BMBS). A bamboo pipe, metal pipe and granite block were buried in the FMBS and BMBS. Physico-chemical parameters of the water which includes temperature (22.74 – 26.06 °C), salinity (0.07 – 15.72 psu), conductivity (153 – 25,420 μS/cm and resistivity (5.6 x 103 – 6.6 x 103 Ω-cm) were measured. Dipole-dipole array using inter-electrode separation (a) of 5 cm and 1≤n≤5 was adopted. Resistivity measurements obtained were processed and inverted using non-linear least-square optimization technique. We present obtained results as contoured 2D resistivity structures. FSM and FMBS were characterized by relatively high resistivity values compared with the BSM and BMBS. The buried materials were effectively resolved in the freshwater models, compared to the brackish water model due to higher contrast in resistivity between the material and saturating fluid. The bamboo pipe was not effectively delineated in the brackish water medium, as a result of the low resistivity contrast which exists between the medium and the material. Zones with high compaction are characterized by high resistivity values, while concealed channel structures were characterized by low compaction. Estimated depth were exact for the freshwater models but was over estimated by `∼12.5% in the brackish water models. Marine electrical resistivity method shows great potential in mapping structures buried in medium with high contrasting resistivity values, and sedimentary structures with varying degree ofcompaction.

Optimum Design of Reinforced Concrete Box Culvert by Using Deterministic Approach

An optimum safe design is achieved for a reinforced concrete box culvert considering hydraulic, geotechnical and structural requirements and prices. To reach this intention, an optimization technique methodology is applied in estimation a safe design with a minimum price. The non-linear constrained optimization problem is generally expressed in terms of an objective function and constraints. Eleven design variables and forty constraint equations were employed in this work, with the total price of reinforced concrete box culvert as the objective function. The solution is obtained by means of developed computer software written in Visual BASIC. The computer software considers the Sequential Unconstrained Minimization Technique (SUMT), which is one of non-linear optimization techniques. In order to evaluate the influence of the input parameters on the box culvert design, the optimization program has been modified. The input design parameters are the discharge, head loss, length, entrance loss coefficient, Manning’s roughness coefficient, angle of internal friction of soil, compressive strength of concrete, yield strength of steel, unit price of concrete, unit price of excavation, unit price of filling and unit price of steel. A parametric study is formed to examine the influence and significance of each input design parameter on the total price of box culvert.

Optimal Estimation of the Roughness Coefficient and Friction Factor of a Pipeline

Abstract This work addresses the estimation of two interrelated parameters of the fluid flow in pipes. First, a numerical and experimental evaluation of some proposed methods to compute the friction factor in turbulent regime is presented. Special attention is given to an explicit solution obtained through the Lambert W-function. Subsequently, a method to estimate the roughness coefficient using nonlinear optimization techniques is proposed, which then allows determining the friction factor from it. Numerical tests were performed for a wide range of operating points of a pipeline. In order to validate the proposed approach, experimental analysis was carried out on a pipeline pilot-plant. The results show the applicability and effectiveness of the proposed method.

Two methods to approximate the Koopman operator with a reservoir computer.

The Koopman operator provides a powerful framework for data-driven analysis of dynamical systems. In the last few years, a wealth of numerical methods providing finite-dimensional approximations of the operator have been proposed [e.g., extended dynamic mode decomposition (EDMD) and its variants]. While convergence results for EDMD require an infinite number of dictionary elements, recent studies have shown that only a few dictionary elements can yield an efficient approximation of the Koopman operator, provided that they are well-chosen through a proper training process. However, this training process typically relies on nonlinear optimization techniques. In this paper, we propose two novel methods based on a reservoir computer to train the dictionary. These methods rely solely on linear convex optimization. We illustrate the efficiency of the method with several numerical examples in the context of data reconstruction, prediction, and computation of the Koopman operator spectrum. These results pave the way for the use of the reservoir computer in the Koopman operator framework.

Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

A seller-buyer supply chain model with fixed and variable labour capacity along with advertising cost and demand

In this paper, supply chain models of single seller and single buyer are proposed in crisp and fuzzy environment to maximise the profit under a manageable inventory policy. These models incorporate the advertising expenditure a part of which is shared by the seller to cooperate with the buyer to have a overall compromised gain of the entire chain. In this case shortages are not permitted so that the rate of production is always greater than or equal to the rate of demand. Keeping all these in view, cooperative and non-cooperative seller-buyer models are developed with fixed and variable labour capacity. The non-cooperative model is solved by seller-buyer Stackelberg strategy while the cooperative problem is solved for pareto optimal solution. Numerical examples are considered to verify the model and the nonlinear problem is solved using nonlinear optimisation technique.

A Faster Procedure for Estimating CFA Models Applying Minimum Distance Estimators with a Fixed Weight Matrix

ABSTRACT This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization techniques. Our implementation differs from the standard approach by utilizing a two-step estimation procedure. In the first step, only a subset of the parameters are estimated using non-linear optimization. In the second step, the remaining parameters are obtained using numerically efficient linear least squares (LLS) methods. Through examples, it is demonstrated that the proposed implementation of MD estimators may be considerably faster than what the standard implementation offer. The proposed procedure will be of particular interest in computationally intensive applications such as simulation, bootstrapping, and other procedures involving re-sampling.

Gamma spectrum stabilization method based on nonlinear least squares optimization

To suppress spectral drift in lengthy gamma spectrum measurements, a convenient and practical spectrum stabilization method with software-only adjustment is studied. The gamma energy spectra are recorded in consecutive small time intervals. The drifts in different time intervals relative to the first reference spectrum are estimated with a full spectrum nonlinear optimization technique, and the drifted spectra are corrected and accumulated to form a drift-free spectrum. This method needs neither hardware adjustment such as high voltage or electronics gain, nor peak location. Presented experimental results show that the stabilization accuracy can be significantly improved using this approach.

Assessment of red-edge vegetation descriptors in a modified water cloud model for forward modelling using Sentinel – 1A and Sentinel – 2 satellite data

ABSTRACT This study investigates the potential of different vegetation descriptors (V) in the modified water cloud model (MWCM), with a focus on comparing the Red – Edge vegetation indices (VI) based and other vegetation descriptors using Sentinel – 1A and Sentinel − 2 satellite data. In order to reduce the influences of vegetation and roughness effectively, a soil geometrical model of equivalent roughness was coupled with a MWCM for the simulation in forward modelling. The optimum value of vegetation extinction coefficient (K VI) and dense vegetation indices () of modified Beer’s law were calculated using non-linear least square optimization technique. After the parameterization of MWCM, the five different types of V for wheat crop were tested for the simulation of backscattering coefficients () at VV polarization. The higher statistical performance indices like coefficient of determination (R2 = 0.96), root-mean-square error (RMSE = 0.17 (dB)) and Nash sutcliffe efficiency (NSE = 0.93) were found for the case of Red-Edge-based vegetation descriptors (for VI = NDVIRE) than others in MWCM. Therefore, this methodology reveals that the VI = NDVIRE by modified Beer’s law can be used effectively as the vegetation parameter in the MWCM for accurate simulation in forward direction over vegetation-covered areas.

Loop-Box: Multiagent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping.

In this article, we present a multiagent framework for real-time large-scale 3-D reconstruction applications. In SLAM, researchers usually build and update a 3-D map after applying nonlinear pose graph optimization techniques. Moreover, many multiagent systems are prevalently using odometry information from additional sensors. These methods generally involve extensive computer vision algorithms and are tightly coupled with various sensors. We develop a generic method for the key challenging scenarios in multiagent 3-D mapping based on different camera systems. The proposed framework performs actively in terms of localizing each agent after the first loop closure between them. It is shown that the proposed system only uses monocular cameras to yield real-time multiagent large-scale localization and 3-D global mapping. Based on the initial matching, our system can calculate the optimal scale difference between multiple 3-D maps and then estimate an accurate relative pose transformation for large-scale global mapping.

E-rickshaws on urban streets: sustainability issues and policies

PurposeIn urban streets of developing countries, informal e-rickshaw operation can contribute to the development of economically viable, citizen-oriented and environmentally friendly transport systems. Even though informal e-rickshaw operation is important, significant gaps are apparent in the literature in relation to e-rickshaw sustainability issues and policies, and the development of a methodology grounded in a robust theoretical base. The current paper aims to address these gaps by integrating stakeholder theory (ST) and resource dependency theory (RDT) as we consider the linkage between e-rickshaw operation and the sustainability concept.Design/methodology/approachWe apply fuzzy quality function deployment (QFD), integrated with the 0–1 non-linear optimisation technique, to identify optimal policies for the e-rickshaw informal sector on Khulna city's urban streets in Bangladesh.FindingsThe results suggest that optimal policies should be formalised to regularise e-rickshaw operation. Appropriate supervision and monitoring; infrastructural support for battery charging, parking, battery recycling and solar panel installation; and training and development should all be provided.Research limitations/implicationsThe theoretical and managerial implications of our results are discussed, with future research suggestions presented.Originality/valueTo the best of our knowledge, no previous study has used ST and RDT in e-rickshaw sector research. We recognised that the stakeholder settings for sustainability policies can well influence the behaviour of informal e-rickshaw operators while allotting the necessary resources to this sector. This move assists researchers and policymakers to understand the stakeholders and resource dependency sustainability requirements of this sector and the implementation of the required policies.

Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model

Abstract In this paper, nonlinear block-oriented models, namely Neural Wiener (NW) and Neural Hammerstein (NH) model, are developed to simulate Low density polyethylene (LDPE) tubular reactor process. The desired simulated outputs of the reactor model are LDPE polymer conversion and melt flow index (MFI). Generally, a nonlinear block-oriented model consists of dynamic linear block and static nonlinear element, which are identified using nonlinear optimization techniques. A modified Neural Wiener (M−NW) with additional input scheme is also considered during the model development stage. In order to generate data for the model identification process, a steady state model of the LDPE polymerization process is developed using Aspen Plus and then converted into dynamic state using Aspen Dynamic software. Based on the model validation results, M−NW has outperformed the other two models with the coefficient of determination (R2) of 0.993 for polymer conversion and 0.986 for MFI results. Moreover, the simulation results for NW and NH models have shown a comparable performance in modeling the polymer conversion. However, the NW model has performed better in predicting MFI with R2 0.984 compared to than the NH model with R2 0.955. Thus, based on the simulation results, the application of nonlinear block-oriented models such as the M−NW and NW model in simulating the LDPE polymerization process is well justified. Such models can be further implemented in model-based control scheme or as soft sensor.

Automatic balance mechanisms for notional defined contribution pension systems guaranteeing social adequacy and financial sustainability: an application to the Italian pension system

Since the mid 1990s some European countries (including Italy) implemented a Notional Defined Contribution (NDC) pension system. Such a system is based on pay-as-you-go funding, while the pension amount is a function of the individual lifelong contribution. Despite many appealing features, the NDC system presents some drawbacks: first, it is vulnerable to demographic and economic shocks compromising the financial sustainability; second, it could fail to guarantee adequate pension benefits to pensioners. In order to reduce the first limit, automatic balance mechanisms (ABMs) have been proposed in literature and also implemented in Sweden, while solutions that combine financial sustainability and social adequacy have been applied only in a pay-as-you-go point system. The aim of this paper is to insert into the Italian NDC architecture ABMs that preserve social adequacy under financial sustainability constraints. Godinez-Olivares et al. (Insur Math Econ 69:117–126, 2016) built ABMs for a Defined Benefit pension system using nonlinear optimization techniques to calculate the optimal paths of the control variables representing the main drivers of the system: contribution rate, retirement age and indexation of pensions. Following this line of research, we have developed a nonlinear optimization model for the Italian NDC system based on three control variables: pensions indexation, notional rate and contribution rate. The objective function considers both social adequacy and contribution rate sustainability, under liquidity and sustainability constraints. In the numerical application we apply the model to the Italian pension system and test the sensitivity of the results to different economic scenarios and objective function parameters.

Aging-Aware Optimal Energy Management Control for a Parallel Hybrid Vehicle Based on Electrochemical-Degradation Dynamics

Hybrid electric vehicles offer the best alternative to gasoline-only powered vehicles as they combine a conventional propulsion system with an electric propulsion system. A supervisory controller is needed to optimally manage the energy on-board. Published works on this topic have mainly focused on strategies aimed at minimizing the fuel consumption. In this article, we address the problem of designing a supervisory controller that achieves minimum fuel consumption while optimally preserving battery life. Electrochemical degradation dynamics are used in the multi-objective problem formulation to accurately capture, and control battery performance, and aging during the control design phase. The electrochemical degradation model accounts for the electrolyte dynamics to capture high C-rate operation of the battery which are properl in charge sustaining hybrid powertrains. We resort to the optimal control formalism, and nonlinear optimization techniques along with the full discretization approach (in the state, and in the control) to cast the energy management problem into a large scale non-linear programming problem, that is able to deal with multi-scale dynamics, namely from the stiff electrolyte battery dynamics to map-based slow dynamics of the actuators. Numerical simulations conducted over four different standard driving cycles (with, and without road grades) show that our aging-aware energy management approach is able to significantly reduce the deterioration of the battery, while retaining very good fuel reduction performance.

Flight Path Planning of Solar-Powered UAV for Sustainable Communication Relay

Communication is a key aspect of modern life. Unfortunately, when natural disasters occur, the communication system and infrastructure of a city may be partially or even completely destroyed. In this case, communication becomes a crucial part of search-and-rescue missions. This study develops an aerial communication relay platform as an effective solution for communication loss during a natural disaster. The model used considers the aircraft altitude and attitude, which affect the energy acquisition and consumption, and the signal fading effects. The flight path is planned by adopting a nonlinear optimization technique, i.e., the Hermite-Simpson collocation method. For a realistic communication model regarding urban signal loss and path propagation, the building deployment of an area of radius 2km in two cities in South Korea (Seoul and Jeonju) was obtained. Simulation experiments for the different urban environments were performed to test the communication reliability, focusing on the relation between the unmanned aerial vehicle and ground users. Through the simulation, the optimal flight paths in high-rise urban and urban microcell environment were obtained. The obtained results were later compared with a baseline simulation composed of a steady-flight circular path. The generated flight path proves the feasibility of endurance flights for low-altitude communication aid aircrafts using a unified model for signal fading alongside solar power energy acquisition into the case study.

Optimal Energy Utilization for a Solar-Powered Aircraft using Sliding Mode-Based Attitude Control

In this article, an energy optimal dynamic attitude for a solar-powered aircraft is determined and implemented using the finite-time sliding-mode approach. A nonlinear constrained optimization technique has been considered to determine the optimal attitude of the aircraft for given a geographical location, solar position, heading direction, and other flight conditions for clear sky conditions. The optimization is performed so as to travel in the fastest possible way from one location to another location without utilizing the battery power, provided the solar energy is sufficient for the flight. The gain in the velocity due to the attitude optimization is validated with the test flight results of solar unmanned aerial vehicle (UAV) Maraal, which demonstrate the efficacy of the approach proposed here. Computational fluid dynamics (CFD) simulation is carried out to estimate aerodynamic forces acting on the aircraft at different sideslip angles. The developed aircraft dynamics incorporates the nonlinearities associated with relatively large aileron and rudder deflections. To obtain the desired attitude, a sliding-mode-based control scheme is considered. A power rate reaching law is applied to avoid chattering in the controls. Finite-time stability of the closed-loop system is discussed and it is shown that the attitude angles reach the desired values in finite time.

A single-stage cleaner production system with random defective rate and remanufacturing

This paper investigates an impact of random defective rates in an imperfect production system with multiple products and planned backorders in a single-stage production system. The purpose of this study is to control a single-stage cleaner production system with a random defective rate for multiple products. Considering these, the model becomes a non-linear constraint problem. Based on different distribution functions of random defective rates, to eliminate the defective items from the system, five special cases for effective system management are considered. A non-linear optimization technique is utilized to solve the model and obtain the global optimum solution of cleaner production system with multiple products and a global optimum solution. Numerical examples, graphical representations, and sensitivity analysis are given to illustrate the model. Numerical studies prove that the cost related with a Chi-square distribution gives the least cost and cost related with a beta distribution gives the maximum cost, whereas the literature shows a triangular distribution gives the minimum cost.

Advanced Non-linear Mathematical Model for the Prediction of the Activity of a Putative Anticancer Agent in Human-to-mouse Cancer Xenografts.

Mathematical models have long been considered as important tools in cancer biology and therapy. Herein, we present an advanced non-linear mathematical model that can predict accurately the effect of an anticancer agent on the growth of a solid tumor. Advanced non-linear mathematical optimization techniques and human-to-mouse experimental data were used to develop a tumor growth inhibition (TGI) estimation model. Using this mathematical model, we could accurately predict the tumor mass in a human-to-mouse pancreatic ductal adenocarcinoma (PDAC) xenograft under gemcitabine treatment up to five time periods (points) ahead of the last treatment. The ability of the identified TGI dynamic model to perform satisfactory short-term predictions of the tumor growth for up to five time periods ahead was investigated, evaluated and validated for the first time. Such a prediction model could not only assist the pre-clinical testing of putative anticancer agents, but also the early modification of a chemotherapy schedule towards increased efficacy.

Vibration Parameters Estimation by Blade Tip-Timing in Mistuned Bladed Disks in Presence of Close Resonances

The present paper is focused on the post processing of the data coming from the Blade Tip-Timing (BTT) sensors in the case where two very close peaks are present in the frequency response of the vibrating system. This type of dynamic response with two very close peaks can occur quite often in bladed disks. It is related to the fact that the bladed disk is not perfectly cyclic symmetric and the so called “mistuning” is present. A method based on the fitting of the BTT sensors data by means of a 2 degrees of freedom (2DOF) dynamic model is proposed. Nonlinear least square optimization technique is employed for identification of the vibration characteristics. A numerical test case based on a lump parameter model of a bladed disk assembly is used to simulate different response curves and the corresponding sensors signals. The Frequency Response Function (FRF) constructed at the resonance region is compared with the traditional Sine fitting results, the resonance frequencies and damping values estimated by the fitting procedure are also reported. Accurate predictions are achieved and the results demonstrate the considerable capacity of the 2DOF method to be used as a standalone or as a complement to the standard Sine fitting method.