Kinematic modeling of flexure hinges and compliant mechanisms

In this paper, the kinematic performance of flexure hinges and compliant mechanisms calculated by conventional modeling techniques are compared. As these exhibit certain drawbacks with regard to control strategies, mainly large number of degrees of freedom or unacceptable errors, a novel modeling approach for flexure hinges is presented. Instead of the entire flexure hinge only its significant regions are modeled by 3-D structural solids. These master patterns are positioned appropriately and connected by rigid constraint conditions to build a compliant mechanism. The resulting model is characterized by considerably fewer degrees of freedom than a full solid model as well as a marginal deviation of the deflection compared to that of pseudo-rigid-body models, 3-D tapered finite beam elements and analytical Timoshenko beam theory. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An improved approach to attribute reduction with ant colony optimization

Attribute reduction problem (ARP) in rough set theory (RST) is an NPhard one, which is difficult to be solved via traditionally analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm, named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction.     

An optimization approach to resolving circular shareholding in large business groups

Circular shareholding refers to a situation where a series of capital contributions made by companies in a family business group establish a chain of shareholdings. For example, a circular shareholding is formed when company A owns stock in company B, company B owns stock in company C, and company C owns stock in company A. In Korea, the practice of circular shareholding in large family-controlled business groups may give the principal families higher control over member firms and more opportunities to pursue their own interest at the expense of other shareholders. For this reason, the government of Korea has encouraged large conglomerates to gradually eliminate their circular shareholdings. However, there has been no research as to which shareholdings out of the complicated ownership structure should be cleared in order to resolve the issue of circular shareholding. In this paper, we propose optimization models to address the problem. Of the proposed integer programming models that can eliminate circular shareholding, one maximizes the sum of cash-flow rights while another maximizes the sum of voting rights. The proposed models have been applied to Korean family-controlled business groups, and the results are included herein. To the best knowledge of the authors, this research is the first study to apply optimization theory to the problem of resolving circular shareholding.     

Surrogate modeling for geometry optimization in material design

We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An application of a hybrid approach to modeling a flexible manufacturing system

Both analytic and simulation models were used to analyze the capabilities and requirements of an automated circuit card manufacturing system. Analytic models were used to determine the sensitivity of the measures of effectiveness (MOEs) to various design parameters. This analysis gave approximate results and bounded the range of input parameters for the simulation model. A detailed simulation model was required for use during both the design and production phases of the project. This simulation model incorporated only those variables to which the MOEs are most sensitive, and provided additional features to observe system behavior. The benefits and appropriate uses for each class of models are discussed.     

An optimization approach to gene stacking

We present a multi-objective integer programming model for the gene stacking problem, which is to bring desirable alleles found in multiple inbred lines to a single target genotype. Pareto optimal solutions from the model provide strategic stacking schemes to maximize the likelihood of successfully creating the target genotypes and to minimize the number of generations associated with a stacking strategy. A consideration of genetic diversity is also incorporated in the models to preserve all desirable allelic variants in the target population. Although the gene stacking problem is proved to be NP-hard, we have been able to obtain Pareto frontiers for smaller sized instances within one minute using the state-of-the-art commercial computer solvers in our computational experiments.     

A robust-CVaR optimization approach with application to breast cancer therapy

We present a framework to optimize the conditional value-at-risk (CVaR) of a loss distribution under uncertainty. Our model assumes that the loss distribution is dependent on the state of some system and the fraction of time spent in each state is uncertain. We develop and compare two robust-CVaR formulations that take into account this type of uncertainty. We motivate and demonstrate our approach using radiation therapy treatment planning of breast cancer, where the uncertainty is in the patient’s breathing motion and the states of the system are the phases of the patient’s breathing cycle. We use a CVaR representation of the tails of the dose distribution to the points in the body and account for uncertainty in the patient’s breathing pattern that affects the overall dose distribution.     

An analytical approach to global optimization

Global optimization problems with a few variables and constraints arise in numerous applications but are seldom solved exactly. Most often only a local optimum is found, or if a global optimum is detected no proof is provided that it is one. We study here the extent to which such global optimization problems can be solved exactly using analytical methods. To this effect, we propose a series of tests, similar to those of combinatorial optimization, organized in a branch-and-bound framework. The first complete solution of two difficult test problems illustrates the efficiency of the resulting algorithm. Computational experience with the programbagop, which uses the computer algebra systemmacsyma, is reported on. Many test problems from the compendiums of Hock and Schittkowski and others sources have been solved.The research of the first and the third authors has been supported by AFOSR grants #0271 and #0066 to Rutgers University. Research of the second author has been supported by NSERC grant #GP0036426 and FCAR grants #89EQ4144 and #90NC0305.     

一种改进的几何活动轮廓模型

针对传统的几何活动轮廓模型在分割具有凹形边界时,演化曲线不能准确定位的缺点,提出了一种改进的几何活动轮廓模型,该模型通过在原模型的演化方程中增加一个正的常数项,使得演化曲线在未到达目标边界时,加速曲线演化,而在到达边界附近时,该加速项逐渐减小为零,从而能够很好的完成对凹形边界的分割.实验证明,该方法不仅能够分割具有凹形边界的目标,还能够使演化过程加速,提高几何活动轮廓模型的分割速度.     

An agent-based approach to modeling insider threat

This paper describes the modeling of the potential of an organization to develop an insider threat given certain attributes of its culture. The model represents all employees of the organization and their likelihood of becoming an insider threat. Each employee is instantiated in an agent-zero construct, which accounts for affective, rational, and social behavioral influences. The main driver of behavior is the employee’s level of disgruntlement against the organization. The simulation is run over a period of 10 years and the total number of employees that exceed a certain threshold of becoming an insider threat are computed. This number is compared with survey data on work force ethics as a measure of validity of the simulation results.     

An optimization approach to partitional data clustering

Scalability of clustering algorithms is a critical issue facing the data mining community. One method to handle this issue is to use only a subset of all instances. This paper develops an optimization-based approach to the partitional clustering problem using an algorithm specifically designed for noisy performance, which is a problem that arises when using a subset of instances. Numerical results show that computation time can be dramatically reduced by using a partial set of instances without sacrificing solution quality. In addition, these results are more persuasive as the size of the problem is larger.     

An optimization approach to multiple sequence alignment

The problem of multiple sequence alignment is recast as an optimization problem using Markov decision theory. One seeks to minimize the expected or average cost of alignment subject to data-derived constraints. In this setting, the problem is equivalent to a linear program which can be solved efficiently using modern interior-point methods. We present numerical results from an implementation of the algorithm for protein sequence alignment     

An improved SQP algorithm for inequality constrained optimization

In this paper, the feasible type SQP method is improved. A new algorithm is proposed to solve nonlinear inequality constrained problem, in which a new modified method is presented to decrease the computational complexity. It is required to solve only one QP subproblem with only a subset of the constraints estimated as active per single iteration. Moreover, a direction is generated to avoid the Maratos effect by solving a system of linear equations. The theoretical analysis shows that the algorithm has global and superlinear convergence under some suitable conditions. In the end, numerical experiments are given to show that the method in this paper is effective.This work is supported by the National Natural Science Foundation (No. 10261001) and Guangxi Science Foundation (No. 0236001 and 0249003) of China. Acknowledgement.We would like to thank one anonymous referee for his valuable comments and suggestions, which greatly improved the quality of this paper.     

An improved trust region method for unconstrained optimization

In this paper,we propose an improved trust region method for solving unconstrained optimization problems.Different with traditional trust region methods,our algorithm does not resolve the subproblem within the trust region centered at the current iteration point,but within an improved one centered at some point located in the direction of the negative gradient,while the current iteration point is on the boundary set.We prove the global convergence properties of the new improved trust region algorithm and give the computational results which demonstrate the effectiveness of our algorithm.     

An improved univariate global optimization algorithm with improved linear lower bounding functions

Recently linear lower bounding functions (LLBF's) were proposed and used to find -global minima. Basically an LLBF over an interval is a linear function which lies below a given function over the interval and matches the function value at one end point. By comparing it with the best function value found, it can be used to eliminate subregions which do not contain -global minima. To develop a more efficient LLBF algorithm, two important issues need to be addressed: how to construct a better LLBF and how to use it efficiently. In this paper, an improved LLBF for factorable functions overn-dimensional boxes is derived, in the sense that the new LLBF is always better than those in [3] for continuously differentiable functions. Exploration of the properties of the LLBF enables us to develop a new LLBF-based univariate global optimization algorithm, which is again better than those in [3]. Numerical results on some standard test functions indicate the high potential of our algorithm.This work was supported in part by VLSI Technology Inc. and Tyecin Systems Inc. through the University of California MICRO proram with grant number 92-024.     

An implicit function approach to constrained optimization with applications to asymptotic expansions

In this article, an unconstrained Taylor series expansion is constructed for scalar-valued functions of vector-valued arguments that are subject to nonlinear equality constraints. The expansion is made possible by first reparameterizing the constrained argument in terms of identified and implicit parameters and then expanding the function solely in terms of the identified parameters. Matrix expressions are given for the derivatives of the function with respect to the identified parameters. The expansion is employed to construct an unconstrained Newton algorithm for optimizing the function subject to constraints.Parameters in statistical models often are estimated by solving statistical estimating equations. It is shown how the unconstrained Newton algorithm can be employed to solve constrained estimating equations. Also, the unconstrained Taylor series is adapted to construct Edgeworth expansions of scalar functions of the constrained estimators. The Edgeworth expansion is illustrated on maximum likelihood estimators in an exploratory factor analysis model in which an oblique rotation is applied after Kaiser row-normalization of the factor loading matrix. A simulation study illustrates the superiority of the two-term Edgeworth approximation compared to the asymptotic normal approximation when sampling from multivariate normal or nonnormal distributions.     

An application of DPCA to oil data for CBM modeling

In multivariate time series analysis, dynamic principal component analysis (DPCA) is an effective method for dimensionality reduction. DPCA is an extension of the original PCA method which can be applied to an autocorrelated dynamic process. In this paper, we apply DPCA to a set of real oil data and use the principal components as covariates in condition-based maintenance (CBM) modeling. The CBM model (Model 1) is then compared with the CBM model which uses raw oil data as the covariates (Model 2). It is shown that the average maintenance cost corresponding to the optimal policy for Model 1 is considerably lower than that for Model 2, and when the optimal policies are applied to the oil data histories, the policy for Model 1 correctly indicates almost twice as many impending system failures as the policy for Model 2.     

An interactive approach for vector optimization problems

This paper deals with an interactive approach for vector optimization problems. The best compromise solutions are obtained using one of the methods which depends on the global preference, namely Geoffrion's method. The stability set of the first kind is analyzed and used to reduce the parametric space of the problem. An example is given for the sake of illustration.     

A robust approach for modeling limited observability in bilevel optimization

Many applications of bilevel optimization contain a leader facing a follower whose reaction deviates from the one expected by the leader due to some kind of bounded rationality. We consider bilinear bilevel problems with follower's response uncertainty due to limited observability regarding the leader's decision and exploit robust optimization to model the decision making of the follower. We show that the robust counterpart of the lower level allows to tackle the problem via the lower level's KKT conditions.