Analog circuits optimization based on evolutionary computation techniques

This paper presents a new design automation tool, based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.     

Rotational spectra, nuclear quadrupole hyperfine tensors, and conformational structures of the mustard gas simulent 2-chloroethyl ethyl sulfide

Rotational spectra have been recorded for both the ³⁵Cl and ³⁷Cl isotopic forms of two structural conformations of 2-chloroethyl ethyl sulfide (CEES). The rotational constants of the ³⁵Cl and ³⁷Cl isotopomers were used to identify the conformational isomers. A total of 236 hyperfine transitions have been assigned for 47 rotational transitions of the ³⁵Cl isotope of a GGT conformer, and 146 hyperfine have been assigned for 37 rotational transitions of the ³⁷Cl isotopomer. For the second conformer, a total of 128 (110) hyperfine and 30 (28) rotational transitions have also been assigned to the ³⁵Cl (³⁷Cl) isotopes of a TGT conformation. The extensive hyperfine splitting data, measured to high resolution with a compact Fourier transform microwave spectrometer, were used to determine both the diagonal and off-diagonal elements of the ³⁵Cl and ³⁷Cl nuclear quadrupole coupling tensors in the inertial tensor principal axis system. The experimental rotational constant data, as well as the ³⁵Cl and ³⁷Cl nuclear quadrupole coupling tensors, were compared to the results from 27 optimized ab initio (HF/6-311++G^∗∗ and MP2/6-311++G^∗∗) model structures.     

Shannon sampling II: Connections to learning theory

We continue our study [S. Smale, D.X. Zhou, Shannon sampling and function reconstruction from point values, Bull. Amer. Math. Soc. 41 (2004) 279–305] of Shannon sampling and function reconstruction. In this paper, the error analysis is improved. Then we show how our approach can be applied to learning theory: a functional analysis framework is presented; dimension independent probability estimates are given not only for the error in the L² spaces, but also for the error in the reproducing kernel Hilbert space where the learning algorithm is performed. Covering number arguments are replaced by estimates of integral operators.     

基于集成最小二乘支持向量机方法的近红外光谱分析

针对近红外（Near Infrared,NIR）光谱测量中的小样本问题。本文提出了一种集成最小二乘支持向量机（Ensemble Least Squares Support Vector Machine,ELS-SVM）新算法。首先使用随机子空间算法（Random Subspace Method,RSM）原始高维变量空间划分为若干个低维度的子空间,然后分别在各个子空间建立最小二乘支持向量机（LS-SVM）模型,最后构造一个集成结果来进行预测。针对一批柴油样本的实验结果表明,本法对柴油十六烷值的预测精度优于传统的LS-SVM方法。     

Development of an Instrument to Assess Attitudes Toward Science,Technology, Engineering,and Mathematics (STEM)

There is a need for more students to be interested in science, technology, engineering, and mathematics (STEM) careers to advance U.S. competitiveness and economic growth. A consensus exists that improving STEM education is necessary for motivating more students to pursue STEM careers. In this study, a survey to measure student (grades 4–6) attitudes toward STEM and STEM careers was developed and administered to 662 students from two STEM‐focused and three comprehensive (non‐STEM‐focused) schools. Cronbach's alphas for the whole survey and subscales indicated a high internal consistency. Statistically significant difference in means between students attending the STEM‐focused and comprehensive schools on the two subscales of the survey and the overall survey were found. However, the explained variance for these results was approximately 1%. The survey is a useful tool to assess efficacy of STEM education programs on student attitudes toward STEM and STEM careers.     

Single-machine group scheduling with processing times dependent on position,starting time and allotted resource

This article considers scheduling problems on a single machine with learning effect, deteriorating jobs and resource allocation under group technology (GT) assumption. We assume that the actual processing time of a job depends on the job position, the group position, the starting time and the amount of resource allocated to them concurrently, and the actual setup times of groups depend on the group position and the amount of resource allocated to them concurrently. Two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost. We prove that the problems have polynomial solutions under the condition that the number of jobs in each group are the same.     

Scheduling deteriorating jobs with a learning effect on unrelated parallel machines

In this study we consider unrelated parallel machines scheduling problems with learning effect and deteriorating jobs, in which the actual processing time of a job is a function of joint time-dependent deterioration and position-dependent learning. The objective is to determine the jobs assigned to corresponding each machine and the corresponding optimal schedule to minimize a cost function containing total completion (waiting) time, total absolute differences in completion (waiting) times and total machine load. If the number of machines is a given constant, we show that the problems can be solved in polynomial time under the time-dependent deterioration and position-dependent learning model.     

Learning AMP chain graphs and some marginal models thereof under faithfulness

This paper deals with chain graphs under the Andersson–Madigan–Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to. Moreover, we show that the extension of Meek's conjecture to AMP chain graphs does not hold, which compromises the development of efficient and correct score + search learning algorithms under assumptions weaker than faithfulness.We also study the problem of how to represent the result of marginalizing out some nodes in an AMP CG. We introduce a new family of graphical models that solves this problem partially. We name this new family maximal covariance–concentration graphs because it includes both covariance and concentration graphs as subfamilies.     

Evaluation of fuzzy regression models by fuzzy neural network

In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Incorporating the learning effect into data envelopment analysis to measure MSW recycling performance

The effect of organizational learning, which results in continuous improvement of organizational performance over time, has been widely discussed. The cumulative learning effect may form as a source of intellectual capital. Thus far, the static data envelopment analysis (DEA) model has not been used to examine the longitudinal learning effect. Therefore, a two-stage approach is developed together with the estimation of a latent learning effect using time-series data; the estimated learning effect is then used as an input in the DEA Slacks-Based Measure (SBM) model. The proposed DEA SBM model can be used to investigate the efficiency of the organizational learning effect of Municipal Solid Waste (MSW) recycling systems.