Set Of Possible Solutions Research Articles

Optimisation of PV-wind-diesel-battery stand-alone systems to minimise cost and maximise human development index and job creation

In this paper we show a multi-objective evolutionary algorithm (MOEA) for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic-wind-diesel-battery) to minimise total net present cost (NPC) and maximise human development index (HDI) and job creation (JC). Optimisation of this kind of system is usually performed considering only the minimisation of cost (NPC or the levelised cost of energy), as well as the emissions and the unmet load in some cases. In this paper, for the first time, we consider the maximisation of HDI and JC as part of optimisation. HDI depends on the consumption of electricity, so the extra energy that can supply the hybrid system can improve the HDI index. JC is different for each technology, obtaining different values for each combination of components in the system. The three objectives are often opposed, so a Pareto-optimisation MOEA is a good option to obtain a set of possible solutions in which no solution is better than another one for all three objectives (optimal Pareto set). We provide an example in the optimisation of a hybrid system to supply electricity to a small community in the Sahrawi refugee camps of Tindouf.

Expert system using multi-objective optimization of the direct current railway power supply system

There are many different aspects to be analyzed when designing a railway infrastructure. The energy system, which withstands the demand for energy from operating trains, must consider many factors to create a functional infrastructure, in terms of demanded energy and cost sustainable. The methodology proposed gives a set of possible solutions to the designer or engineer. On the one hand, this method works with a multi-objective genetic algorithm (NSGA-II), with high time efficiency. The main target of this work is to obtain the best electrical configuration in terms of number and location of substations and characteristics of the overhead line system. On the other hand, best configurations must take into account things such as real railway operation, signalling system, infrastructure, costs linked with environment, maintenance, construction and connection with general electric network, losses of energy dissipated along the catenary. Hence, this methodology must combine all of these skills and integrate it with a railway configuration, modelling and simulation tool, Hamlet developed at CITEF (Research Centre on Railway Technologies by Technical University of Madrid, Spain). After using this methodology, designers will have a set of configurations in order to get the final choice of location of traction substations and type of overhead line system to achieve properly the power demand from trains in railway systems.

Application of Computional Fluid Dynamics for Study of the Occurrence of Aerodynamic Effect for its Application in the Construction of Electric Race Car

In this article the issues related to Computional Fluid Dynamics of the occurrence of innovative aerodynamic effect were presented. Analysis were performed to determine the occurrence of Kammback aerodynamic effect and its application in a shape of a body of the real racing car in order to minimize drag forces of the vehicle. For the analysis, ideal aerodynamic shapes were modeled, subsequently they were subjected to modifications which were used to determine the occurrence of effect. The basic modeled shape was the raindrop shape solid, which is generally regarded as the ideal shape in terms of aerodynamics. The result of analysis was compared with the drag values known from the literature. Afterwards changes in the shape of the base solid were made to verify and determine the optimum Kammback shape, selected from a set of possible solutions, in which the geometrical changes has the lowest difference of values of drag force and drag coefficientCx(Cd)in comparison to the basic raindrop shape. Results of the study were subjected to graphic analysis, especially the distribution of air pressure on the surface of a solid and in a virtual wind tunnel, distribution of the air velocity and the course of air streams around the shape. The results were used to design the body of electric race car. The main objective was to minimize the aerodynamic drag of the vehicle.

Methodology for Multiobjective Optimization of the AC Railway Power Supply System

Electrical dimensioning design in a railway infrastructure has high complexity and is strongly nonlinear. There are several parameters and constraints to take into account. The presented methodology is working to improve the decision making about the design, hence developing an expert system. The final set of possible solutions that the method is achieved is based on a pair of main objectives. On the one hand are installation costs, environmental impact, main electrical components costs such as catenary, traction substations, neutral zones, and the difficulty to connect substation to general electric grid. On the other hand are exploitation costs, such as maintenance costs and energy loss depending on the dimensioning design. A specific line discretization has been developed in order to distribute the critical zones along the line. Integration of a multiobjective genetic algorithm (NSGA-II), hence the code of the genotype, is another highlight. Electrical analysis, railway systems using single alternate current (1 × 25), and a simplification due to the search of the highest peaks of power demanded by the trains during simulations help to minimize the quantity of studies. Every simulation is possible by using a railway simulator, Hamlet, which provides modules such as infrastructure, rolling stock, signaling, and electrical. Designers have the possibility to analyze several scenarios with different railway exploitation critical levels, analyze electrical degraded situations, and finally obtain an optimal Pareto Front, reaching a powerful methodology to help in the electrical dimensioning design.

A context-aware approach in realization of socially intelligent industrial robots

Contemporary industrial environments are usually constrained or limited in order to fit a fast, cheap and non-error prone production. Human-like system capabilities are not generally desirable there. But, recent trends in industrial robotics demand robust, flexible and efficient robots with a certain level of autonomy. Therefore, new and different approaches and perspectives in designing of industrial facilities are required.This paper reveals how a context-based reasoning can be used to achieve an intelligent robot group behavior. In order to achieve adaptivity, self-recovery or scalability of the system, a COgnitive MOdel for the Robot group control (COMOR) is developed. COMOR can be understood as an interpreter used to transform high-level context to low-level data, allowing machines to make context-based decisions.COMOR has three main parts and relies on a simulated Social Capital phenomenon as a feature of people. The first part is used to collect significant information from the environment. The second part is used to provide a set of possible solutions respecting the semantic domain description. The last part of COMOR is used to provide a behavioral component ensuring an optimal solution to given environmental conditions. A cognitive model for a robot work (COMOR) on industrial assignments is proposed.A partial contextual awareness is achieved through three model components.Sensors are placed seamlessly within the environment to ensure ubiquitousness.Formal knowledge is stored in so-called the Core Ontology as a domain description.Social Capital is achieved in Bayesian Network to provide unambiguity in solutions.

COMPSEC, a new tool to derive natural background levels by the component separation approach: application in two different hydrogeological contexts in northern Italy

The assessment of natural background levels (NBLs) is crucial in the management of groundwater resources and the definition of the status of groundwater bodies according to the Water Framework Directive 2000/60/EC. This work presents a new MATLAB code to assess NBLs by the component separation method, which concerns the identification and separation of a natural and an anthropogenic component within geochemical data. To increase the validity of the estimated NBL, the code calculates a set of possible solutions that should then be evaluated by the user. The following evaluation criteria are proposed: (i) stability of solution, (ii) hydrogeochemical reliability, (iii) agreement with the conceptual model and (iv) goodness of fit.The code is tested on groundwater Mn, Fe, As, Ni, Cr(VI) and NH4 concentrations observed in two study areas with different hydrogeological and geochemical features in northern Italy: the multi-layer aquifer of Cremona (lower Po Plain) and the alluvial aquifer of the Aosta Plain (western Alpine sector). The pre-selection method is also applied to estimate NBLs and threshold values are assessed.The NBLs calculated through the component separation method (4292, 27 and 1220μg/L for Fe, As and NH4 in Cremona and 56, 261, 9 and 2.3μg/L for Mn, Fe, Ni and Cr(VI) in Aosta, respectively) approach those estimated by the pre-selection (2798, 25 and 1820μg/L for Fe, As and NH4 in Cremona and 52, 264, 10 and 1.8μg/L for Mn, Fe, Ni and Cr(VI) in Aosta, respectively). This outcome increases the validity of the estimated NBLs. The code showed versatile applicability in different contexts and conditions (e.g., different numbers of samples and monitoring points, different sampling networks). This work highlights the strength of the component separation method although some criticisms are pointed out, mainly related to the superimposition of a log-normal function for modeling the natural component.

The last developments of the digital economy and bitcoins as a ‘stress test’ for the EU VAT system

The BEPS project highlights the relevance of the challenges posed by the digital economy. The OECD notes that, because the digital economy is increasingly becoming the economy itself, it is not feasible to ringfence it from the rest of the economy for tax purposes. To a certain extent, this risk is inherent to the EU VAT system. In this article, the author uses virtual currencies as a ‘stress test’ for the actual EU VAT system, with the aim of pinpointing some of its weaknesses in respect of the digital economy. Having summarised the recent developments within EU and OECD context, the author proposes an analysis of what virtual currencies and mining activities ‘are’ under the current VAT, and how they could be treated. Finally, the author proposes and analyses a set of possible solutions. He goes from solutions based on the interpretation of current VAT legislation, which could be accepted by the CJEU in the pending case Hedqvist (C-264/14), to an innovative idea that would imply a radical change of the c...

Analytical three-point Dixon method: With applications for spiral water-fat imaging.

The goal of this work is to present a new three-point analytical approach with flexible even or uneven echo increments for water-fat separation and to evaluate its feasibility with spiral imaging. Two sets of possible solutions of water and fat are first found analytically. Then, two field maps of the B0 inhomogeneity are obtained by linear regression. The initial identification of the true solution is facilitated by the root-mean-square error of the linear regression and the incorporation of a fat spectrum model. The resolved field map after a region-growing algorithm is refined iteratively for spiral imaging. The final water and fat images are recalculated using a joint water-fat separation and deblurring algorithm. Successful implementations were demonstrated with three-dimensional gradient-echo head imaging and single breathhold abdominal imaging. Spiral, high-resolution T1 -weighted brain images were shown with comparable sharpness to the reference Cartesian images. With appropriate choices of uneven echo increments, it is feasible to resolve the aliasing of the field map voxel-wise. High-quality water-fat spiral imaging can be achieved with the proposed approach.

Enhancing the Bandwidth of Electromagnetic Cloaks Using Multi-Objective Optimization

Reducing the scattering and enhancing the bandwidth (BW) are two desirable aspects in cloak applications. In this paper, we propose their optimization using a simplified objective function, which avoids a costly detailed frequency sweep to get the BW at each iteration. The multi-objective characteristic of our optimization permits us to obtain a set of possible solutions (the Pareto-Front), which represents different compromises between the two objectives. Two examples of dielectric cloaks are presented and discussed.

Three-dimensional gravity inversion using graph theory to delineate the skeleton of homogeneous sources

We developed a new strategy, based on graph theory concepts, to invert gravity data using an ensemble of simple point masses. Our method consisted of a genetic algorithm with elitism to generate a set of possible solutions. Each estimate was associated to a graph to solve the minimum spanning tree (MST) problem. To produce unique and stable estimates, we restricted the position of the point masses by minimizing the statistical variance of the distances of an MST jointly with the data-misfit function during the iterations of the genetic algorithm. Hence, the 3D spatial distribution of the point masses identified the skeleton of homogeneous gravity sources. In addition, our method also gave an estimation of the anomalous mass of the source. So, together with the anomalous mass, the skeleton could aid other 3D methods with promising geometric a priori parameters. Several tests with different values of regularizing parameter were made to bespeak this new regularizing strategy. The inversion results applied to noise-corrupted synthetic gravity data revealed that, regardless of promising starting models, the estimated distribution of point masses and the anomalous mass offered valuable information about the homogeneous sources in the subsurface. Tests on real data from a portion of Quadrilátero Ferrífero, Minas Gerais state, Brazil, were performed for complementary analysis of the proposed inversion method.

WARMER LOCAL INTERSTELLAR MEDIUM: A POSSIBLE RESOLUTION OF THEULYSSES-IBEXENIGMA

Interstellar Boundary Explorer (IBEX) measurements from 2009-2010 identified a set of possible solutions with very tight coupling between the interstellar He inflow longitude, latitude, speed, and temperature. The center of this allowable parameter space suggested that the heliosphere could be moving more slowly and in a slightly different direction with respect to the interstellar medium than indicated by earlier Ulysses observations. In this study we examine data from 2012-2014 and compare results from an analytic analysis and a detailed computer model. For observations where the IBEX spacecraft pointing is near the ecliptic plane, the latest measurements indicate a different portion of IBEX's four-dimensional tube of possible parameters—one that is more consistent with the Ulysses flow direction and speed, but with a much higher temperature. Together, the current combined IBEX/Ulysses values we obtain are V ISM∞ ~ 26 km s–1, λISM∞ ~ 75°, βISM∞ ~ –5°, and T He∞ ~ 7000-9500 K. These indicate that the heliosphere is in a substantially warmer region of the interstellar medium than thought from the earlier Ulysses observations alone, and that this warmer region may be roughly isothermal. However, measurements taken when IBEX was pointing ~5° south of the ecliptic are inconsistent with this solution and suggest a slower speed, lower temperature, and flow direction similar to IBEX's prior central values. IBEX measures much deeper into the tails of the distributions of the inflowing interstellar material than Ulysses did and these observations indicate that the heliosphere's interstellar interaction is likely far more complex and interesting than previously appreciated.

Robust Solutions for Systems of Uncertain Linear Equations

Our contribution is twofold. Firstly, for a system of uncertain linear equations where the uncertainties are column-wise and reside in general convex sets, we show that the intersection of the set of possible solutions and any orthant is convex. We derive a convex representation of this intersection to calculate the ranges of the coordinates. Secondly, we propose two new methods for obtaining robust solutions of systems of uncertain linear equations. The first method calculates the center of the maximum inscribed ellipsoid of the set of possible solutions. The second method minimizes the expected violations with respect to the worst-case distribution. We compare these two new methods both theoretically and numerically with an existing method. The existing method minimizes the worst-case violation. Applications to the input-output model, Colley's Matrix Rankings and Article Influence Scores demonstrate the advantages of the two new methods.

Rotating drops with helicoidal symmetry

See http://youtu.be/Mf4IE8gWcJs for a YouTube video showing part of the results in this paper. We consider helicoidal immersions in the Euclidean space whose axis of symmetry is the z-axis that are solutions of the equation 2 H=\Lambda_0-a 1/2 R^2 where H is the mean curvature of the surface, R is the distance form the point in the surface to the z-axis and a is a real number. We refer to these surfaces as helicoidal rotating drops. We prove the existence of properly immersed solutions that contain the z-axis. We also show the existence of several families of embedded examples. We describe the set of possible solutions and we show that most of these solutions are not properly immerse and are dense in the region bounded by two concentric cylinders. We show that all properly immersed solutions, besides being invariant under a one parameter helicoidal group, they are invariant under a cyclic group of rotations of the variables x and y. The second variation of energy for the volume constrained problem with Dirichlet boundary conditions is also studied.

Optimal Predictive Torque Control of Two PMSM supplied in Parallel on a Single Inverter

This paper presents a new approach of Optimal Predictive Torque Control (OPTC) for two Permanent Magnet Synchronous Machines supplied in parallel on a single mutualized three-leg two-level inverter. In the field of multi synchronous machine control with a single inverter, the classic Predictive Torque Control (PTC) rests on the selection of a configuration of switches among a discrete set of possible solutions by the minimization of a single criterion of performance, sum of the criteria specific to each machine. It leads then to the definition of an applied voltage which results from a compromise between the machines. The method suggested here rests on the calculation of the optimal controls on a continuous set of solutions for each of the two machines separately, and the generation of the final applied voltage via the calculation of a mean value. In addition to the reduction of the highly-reduced computing time, this control law improves the Total Harmonic Distortion (THD) of the line currents.

Mid-Space-Independent Symmetric Data Term for Pairwise Deformable Image Registration.

Aligning a pair of images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the choice of the mid-space. In particular, the set of possible solutions is typically affected by the constraints that are enforced on the two transformations (that deform the two images), which are to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed to define the mid-space for pairwise registration. In this work, we show that by aligning the atlas to each image in the native image space, implicit-atlas-based pairwise registration can be made independent of the mid-space, thereby eliminating the need for anti-drift constraints. We derive a new symmetric cost function that only depends on a single transformation morphing one image to the other, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.

Optimal allocation and adaptive VAR control of PV-DG in distribution networks

The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme.

Closed-Form Solution for Lost-In-Space Visual Navigation Problem

The paper describes a new closed-form solution for the lost-in-space visual navigation problem. Focal plane images of several known beacons are used to determine both position and attitude of a sensor equipped with position-sensing diodes. A preliminary set of possible solutions is obtained without any initial guesses using any three beacons. These solutions employ a closed-form method for finding sensor-to-beacon distances based on their lines of sight. Instead of providing these solutions in terms of polynomial roots, the paper for the first time derives these solutions explicitly and includes a complete error analysis. Using additional beacons, the possible three-beacon solutions are refined, extraneous solutions are discarded, and the best solution is identified via the Gauss–Newton method. The paper also includes numerical examples and a statistical validation of the proposed method.

Solving the traveling salesman problem using simulated annealing

Mathematical optimization or mathematical programming is the choice of the best elements from a set of alternatives, according to certain criteria. Extremely complex problems that have become very popular with the development of computer science can be solved by using Mathematical optimization. If it is not possible to determine the exact solution of the problem using traditional methods, we can approach to problem solving by using heuristic methods. Thereby we can determine an approximate solution. One type of the heuristics are metaheuristics. The metaheuristics are very useful approach to the optimization problems, because finding a large set of possible solutions can lead to a good solution with less computing 'effort ' than algorithms, iterative methods and simple heuristics. Simulated Annealing is a type of metaheuristics inspired by the process of the metals annealing that involves heating and controlled cooling of metals to modify the physical properties due to changes occurring in the internal structure of materials. Simulated annealing is a suitable algorithm for solving NP - hard problems such as the Traveling Salesman Problem. This paper presents the pseudocode for solving the Traveling Salesman Problem using the simulated annealing algorithm.

Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm

ABSTRACTMagnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.

Optimized explicit finite-difference schemes for spatial derivatives using maximum norm

We propose an optimized scheme using the maximum norm and the simulated annealing.The maximum norm offers the largest set of possible solutions for solvers to search.The explicit finite-difference ...