Solving MIN ONES 2-SAT as fast as VERTEX COVER

The problem of finding a satisfying assignment that minimizes the number of variables that are set to 1 is NP-complete even for a satisfiable 2-SAT formula. We call this problem MIN ONES 2-SAT. It generalizes the well-studied problem of finding the smallest vertex cover of a graph, which can be modeled using a 2-SAT formula with no negative literals. The natural parameterized version of the problem asks for a satisfying assignment of weight at most k. In this paper, we present a polynomial-time reduction from MIN ONES 2-SAT to VERTEX COVER without increasing the parameter and ensuring that the number of vertices in the reduced instance is equal to the number of variables of the input formula. Consequently, we conclude that this problem also has a simple 2-approximation algorithm and a 2k - c logk-variable kernel subsuming (or, in the case of kernels, improving) the results known earlier. Further, the problem admits algorithms for the parameterized and optimization versions whose runtimes will always match the runtimes of the best-known algorithms for the corresponding versions of vertex cover. Finally we show that the optimum value of the LP relaxation of the MIN ONES 2-SAT and that of the corresponding VERTEX COVER are the same. This implies that the (recent) results of VERTEX COVER version parameterized above the optimum value of the LP relaxation of VERTEX COVER carry over to the MIN ONES 2-SAT version parameterized above the optimum of the LP relaxation of MIN ONES 2-SAT. (C) 2013 Elsevier B.V. All rights reserved.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.

Role of melt convection on optimization of PCM-based heat sink under cyclic heat load

In this article, we study the thermal performance of phase-change material (PCM)-based heat sinks under cyclic heat load and subjected to melt convection. Plate fin type heat sinks made of aluminum and filled with PCM are considered in this study. The heat sink is heated from the bottom. For a prescribed value of heat flux, design of such a heat sink can be optimized with respect to its geometry, with the objective of minimizing the temperature rise during heating and ensuring complete solidification of PCM at the end of the cooling period for a given cycle. For given length and base plate thickness of a heat sink, a genetic algorithm (GA)-based optimization is carried out with respect to geometrical variables such as fin thickness, fin height, and the number of fins. The thermal performance of the heat sink for a given set of parameters is evaluated using an enthalpy-based heat transfer model, which provides the necessary data for the optimization algorithm. The effect of melt convection is studied by taking two cases, one without melt convection (conduction regime) and the other with convection. The results show that melt convection alters the results of geometrical optimization.

Numerical and experimental investigation and optimization of an open wheeled race car cooling air duct

In this study the cooling performance due to air flow and aerodynamics of the Formula Student open wheeled race car has been investigated and optimized with the help of CFD simulations and experimental validation. The race car in context previously suffered from overheating problems. Flow analysis was carried out based on the detailed race car 3D model (NITK Racing 2012 formula student race car). Wind tunnel experiments were carried out on the same. The results obtained from the computer simulations are compared with experimental results obtained from wind tunnel testing of the full car. Through this study it was possible to locate the problem areas and hence choose the best configuration for the cooling duct. The CFD analysis helped in calculating the mass flow rate, pressure and velocity distribution for different velocities of the car which is then used to determine the heat dissipated by the radiator. Area of flow separation could be visualized and made sure smooth airflow into the radiator core area. This significantly increased the cooling performance of the car with reduction in drag.

Optimization of rheological properties of photopolymerizable alumina suspensions for ceramic microstereolithography

Microstereolithography (MSL) is a rapid prototyping technique to fabricate complex three-dimensional (3D) structure in the microdomain involving different materials such as polymers and ceramics. The present effort is to fabricate microdimensional ceramics by the MSL system from a non-aqueous colloidal slurry of alumina. This slurry predominantly consists of two phases i.e. sub-micrometer solid alumina particles and non-aqueous reactive difunctional and trifunctional acrylates with inert diluent. The first part of the work involves the study of the stability and viscosity of the slurry using different concentrations of trioctyl phosphine oxide (TOPO) as a dispersant. Based on the optimization, the highest achievable solid loadings of alumina has been determined for this particular colloidal suspension. The second part of the study highlights the fabrication of several micro-dimensional alumina structures by the MSL system. (C) 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Optimum steering input determination and path-tracking of all-wheel steer vehicles on uneven terrains based on constrained optimization

In this paper we propose a framework for optimum steering input determination of all-wheel steer vehicles (AWSV) on rough terrains. The framework computes the steering input which minimizes the tracking error for a given trajectory. Unlike previous methodologies of computing steering inputs of car-like vehicles, the proposed methodology depends explicitly on the vehicle dynamics and can be extended to vehicle having arbitrary number of steering inputs. A fully generic framework has been used to derive the vehicle dynamics and a non-linear programming based constrained optimization approach has been used to compute the steering input considering the instantaneous vehicle dynamics, no-slip and contact constraints of the vehicle. All Wheel steer Vehicles have a special parallel steering ability where the instantaneous centre of rotation (ICR) is at infinity. The proposed framework automatically enables the vehicle to choose between parallel steer and normal operation depending on the error with respect to the desired trajectory. The efficacy of the proposed framework is proved by extensive uneven terrain simulations, for trajectories with continuous or discontinuous velocity profile.

Modified roaming optimization for multi-modal optima

This paper explains the algorithm of Modified Roaming Optimization (MRO) for capturing the multiple optima for multimodal functions. There are some similarities between the Roaming Optimization (RO) and MRO algorithms, but the MRO algorithm is created to overcome the problems facing while applying the RO to the problems possessing large number of solutions. The MRO mainly uses the concept of density to overcome the challenges posed by RO. The algorithm is tested with standard test functions and also discussions are made to improve the efficacy of the MRO algorithm. This paper also gives the results of MRO applied for solving Inverse Kinematics (IK) problem for SCARA and PUMA robots.

Transonic flutter, shocks, and parameter effects

There is a drop in the flutter boundary of an aeroelastic system placed in a transonic flow due to compressibility effects and is known as the transonic dip. Viscous effects can shift the lo-cation of the shock and depending on the shock strength the boundary layer may separate leading to changes in the flutter speed. An unsteady Euler flow solver coupled with the structural dynamic equations is used to understand the effect of shock on the transonic dip. The effect of various system parameters such as mass ratio, location of the center of mass, position of the elastic axis, ratio of uncoupled natural frequencies in heave and pitch are also studied. Steady turbulent flow results are presented to demonstrate the effect of viscosity on the location and strength of the shock.

Study on ride comfort assessment of multiple occupants using lumped parameter analysis

Growing consumer expectations continue to fuel further advancements in vehicle ride comfort analysis including development of a comprehensive tool capable of aiding the understanding of ride comfort. To date, most of the work on biodynamic responses of human body in the context of ride comfort mainly concentrates on driver or a designated occupant and therefore leaves the scope for further work on ride comfort analysis covering a larger number of occupants with detailed modeling of their body segments. In the present study, governing equations of a 13-DOF (degrees-of-freedom) lumped parameter model (LPM) of a full car with seats (7-DOF without seats) and a 7-DOF occupant model, a linear version of an earlier non-linear occupant model, are presented. One or more occupant models can be coupled with the vehicle model resulting into a maximum of 48-DOF LPM for a car with five occupants. These multi-occupant models can be formulated in a modular manner and solved efficiently using MATLAB/SIMULINK for a given transient road input. The vehicle model and the occupant model are independently verified by favorably comparing computed dynamic responses with published data. A number of cases with different dispositions of occupants in a small car are analyzed using the current modular approach thereby underscoring its potential for efficient ride quality assessment and design of suspension systems.

Evaluation of the accuracy of an impact load cell using lumped parameter modeling and analysis

The objective of the current study is to evaluate the fidelity of load cell reading during impact testing in a drop-weight impactor using lumped parameter modeling. For the most common configuration of a moving impactor-load cell system in which dynamic load is transferred from the impactor head to the load cell, a quantitative assessment is made of the possible discrepancy that can result in load cell response. A 3-DOF (degrees-of-freedom) LPM (lumped parameter model) is considered to represent a given impact testing set-up. In this model, a test specimen in the form of a steel hat section similar to front rails of cars is represented by a nonlinear spring while the load cell is assumed to behave in a linear manner due to its high stiffness. Assuming a given load-displacement response obtained in an actual test as the true behavior of the specimen, the numerical solution of the governing differential equations following an implicit time integration scheme is shown to yield an excellent reproduction of the mechanical behavior of the specimen thereby confirming the accuracy of the numerical approach. The spring representing the load cell, however,predicts a response that qualitatively matches the assumed load-displacement response of the test specimen with a perceptibly lower magnitude of load.

REDUCING COMPUTATIONAL EFFORT IN SOLVING GEOMECHANICS PROBLEMS WITH UPPER BOUND FINITE ELEMENTS LIMIT ANALYSIS AND LINEAR OPTIMIZATION

This paper presents a simple technique for reducing the computational effort while solving any geotechnical stability problem by using the upper bound finite element limit analysis and linear optimization. In the proposed method, the problem domain is discretized into a number of different regions in which a particular order (number of sides) of the polygon is chosen to linearize the Mohr-Coulomb yield criterion. A greater order of the polygon needs to be selected only in that region wherein the rate of the plastic strains becomes higher. The computational effort required to solve the problem with this implementation reduces considerably. By using the proposed method, the bearing capacity has been computed for smooth and rough strip footings and the results are found to be quite satisfactory.

Relation between the Isentropic Index and the Gruneisen Parameter for Saturated Liquids

We investigate the isentropic index along the saturated vapor line as a correlating parameter with quantities both in the saturated liquid phase and the saturated vapor phase. The relation is established via temperatures such as T-hgmax and T* where the saturated vapor enthalpy and the product of saturation temperature and saturated liquid density attain a maximum, respectively. We obtain that the saturated vapor isentropic index is correlated with these temperatures but also with the saturated liquid Gruneisen parameters at T-hgmax. and T*.

Solving Axisymmetric Stability Problems by Using Upper Bound Finite Elements, Limit Analysis, and Linear Optimization

A numerical formulation has been proposed for solving an axisymmetric stability problem in geomechanics with upper bound limit analysis, finite elements, and linear optimization. The Drucker-Prager yield criterion is linearized by simulating a sphere with a circumscribed truncated icosahedron. The analysis considers only the velocities and plastic multiplier rates, not the stresses, as the basic unknowns. The formulation is simple to implement, and it has been employed for finding the collapse loads of a circular footing placed over the surface of a cohesive-frictional material. The formulation can be used to solve any general axisymmetric geomechanics stability problem.