Modeling energy dependent nuclear reaction probabilities from pointwise measurements including full covariance information

For the design of large and highly complex facilities – like, the international project ITER (http://www.iter.org/) – reliable basic data with well established covariance information (uncertainties, correlations) are indispensable. Procedures to attain the requirements by means of a Bayesian generalized least squares method and recent approaches using theoretical nuclear reaction codes in combination with Monte Carlo techniques will be discussed and results compared. Particular emphasis is given to the procedures of constructing covariance matrices for the experimental data sets as this determines the quality of the result.     

Resource allocation optimization for quantitative service differentiation on server clusters

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers.     

Approximate factorization of multivariate polynomials using singular value decomposition

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total least squares approximation and Gauss–Newton optimization to numerically compute the approximate multivariate factors. We demonstrate on a large set of benchmark polynomials that our algorithms efficiently yield approximate factorizations within the coefficient noise even when the relative error in the input is substantial (10⁻³).     

The solution and duality of imprecise network problems

Duality properties have been investigated by many researchers in the recent literature. They are introduced in this paper for a fully fuzzified version of the minimal cost flow problem, which is a basic model in network flow theory. This model illustrates the least cost of the shipment of a commodity through a capacitated network in terms of the imprecisely known available supplies at certain nodes which should be transmitted to fulfil uncertain demands at other nodes. First, we review on the most valuable results on fuzzy duality concepts to facilitate the discussion of this paper. By applying Hukuhara’s difference, approximated and exact multiplication and Wu’s scalar production, we exhibit the flow in network models. Then, we use combinatorial algorithms on a reduced problem which is derived from fully fuzzified MCFP to acquire fuzzy optimal flows. To give duality theorems, we utilize a total order on fuzzy numbers due to the level of risk and realize optimality conditions for providing some efficient combinatorial algorithms. Finally, we compare our results with the previous worthwhile works to demonstrate the efficiency and power of our scheme and the reasonability of our solutions in actual decision-making problems.     

Hopping on Even Ground and Up Stairs with a Single Articulated Leg

This paper presents a method for generating gaits for a one-legged articulated hopping robot. A static optimization procedure produces the initial joint velocities for the flight phase, using the principle of conservation of angular momentum and assuming (nearly) passive flight. Two novel objective functions for this static optimization enable one to choose different gaits by simply changing a few parameters. A dynamic optimization procedure yields a solution for the flight trajectory that minimizes control effort. The stance phase (when the foot is touching the ground) becomes a standard two point boundary value problem, also solved with a dynamic optimization procedure. During the stance phase, the physical joint limitations, ground reaction forces, and the trajectory of the zero-moment point all constrain the solution. After these single-phase optimizations, a complete-cycle optimization procedure, incorporating both flight and stance phases, further reduces the control effort and balances the motion phases. In simulation, the leg hops on even ground and up stairs, exhibiting energy-efficient and intuitively satisfying gaits.     

Fabrication uncertainties and yield optimization in MEMS tunable capacitors

Intrinsic uncertainties of MEMS fabrication processes can severely affect the performance of devices because the tolerance ranges of these processes are relatively large and improvement of process accuracy is very expensive. Therefore, the analysis of fabrication uncertainties and their outcome on a device performance is a vital task before finalizing the design. In this paper, the effects of process inaccuracy on the performance of MEMS tunable capacitors are studied. Design parameters such as dimensions of electrodes and the initial gap between them and the stiffness of supporting beams are considered as random variables. The variation of these parameters within tolerance ranges drastically alters the capacitor's actual response from the desired one and results in low yield. Hence, design optimization with the objective of maximizing yield in early steps becomes very important. An effective method for yield optimization of MEMS capacitors under given fabrication uncertainties is introduced. The method utilizes aspects of the advanced first-order second-moment (AFOSM) reliability method to find a linearized feasible region to estimate the yield. The yield is calculated directly using the joint cumulative distribution function (CDF) over the tolerance box requiring no numerical integration and avoiding computational complexity. The optimal design verified by Monte-Carlo (M-C) simulation exhibits a significant increase in the yield. The main advantage of this method comparing to other design optimization methods is that the proposed method does not change the design topology or fabrication accuracy. It increases the yield by finding the optimum design variables as demonstrated in this paper.     

一种具有双重进化空间的扩展粒子群优化算法

为了使粒子群优化(PSO)适于求解更多类问题,提出一种由动力空间和制导空间共同进化的改进粒子群优化算法-具有双重进化空间的扩展粒子群优化算法(简记EPSO).在EPSO中,在演化转换映射的作用下,首先将动力空间中对粒子辅助位置的进化转换为制导空间中对主导位置的进化,然后基于对主导位置的择优选择操作实现算法的进化过程.EPSO克服了PSO仅适于求解连续域最优化问题的缺陷,也非常适于求解离散组合优化问题.对于随机3-SAT问题、背包问题和TSP问题,通过与PSO、ACO和GA等算法的计算对比表明:EPSO是一种继承了PSO优点的高效、扩展演化算法.     

Using a style-based ant colony system for adaptive learning

Adaptive learning provides an alternative to the traditional “one size fits all” approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.