A NEW CONSTRAINTS IDENTIFICATION TECHNIQUE-BASED QP-FREE ALGORITHM FOR THE SOLUTION OF INEQUALITY CONSTRAINED MINIMIZATION PROBLEMS

In this paper, we propose a feasible QP-free method for solving nonlinear inequality constrained optimization problems. A new working set is proposed to estimate the active set. Specially, to determine the working set, the new method makes use of the multiplier information from the previous iteration, eliminating the need to compute a multiplier function. At each iteration, two or three reduced symmetric systems of linear equations with a common coefficient matrix involving only constraints in the working set are solved, and when the iterate is sufficiently close to a KKT point, only two of them are involved. Moreover, the new algorithm is proved to be globally convergent to a KKT point under mild conditions. Without assuming the strict complementarity, the convergence rate is superlinear under a condition weaker than the strong second-order sufficiency condition. Numerical experiments illustrate the efficiency of the algorithm.     

Performance and optimum dimensions of flat fins for tube-and-fin heat exchangers: A generalized analysis

The performance of flat fins for tube-fin heat exchangers has been analyzed for both inline and staggered arrangement of tubes. In earlier published studies, regular square and equilateral triangular array tube layouts were considered. No such restriction is put in the present study. The analysis has been done by a semi-analytical technique where the boundary condition at the fin edge is discretely satisfied at a large number of points by a method of collocation. It has also been demonstrated that the approximate results obtained by the sector method closely agree with the prediction of semi-analytical technique. Finally, a generalized scheme of optimization based on Lagrange multiplier technique has been suggested which shows that irrespective of the volume and thickness of the fins, square and equilateral triangular array of tubes are the optimum layout for inline and staggered arrangements, respectively. This result was known so far only intuitively. The optimum dimensions for flat fins for other layout of tubes have also been obtained specifying the ratio of longitudinal to transverse tube pitch.     

Theoretical optimization of analyte collection in analytical supercritical fluid extraction

Summary Optimizing the extracted analyte collection step in analytical supercritical fluid extraction (SFE) is of key importance in achieving high analyte recoveries and extraction efficiencies. Whereas the extraction step in SFE has been well characterized both theoretically and experimentally; the analyte collection step after SFE has few theoretical guidelines, aside from a few empirical studies which have appeared in the literature. In this study, we have applied several theoretical approaches using experimental data to optimize analyte trapping efficiency in SFE. A vapour-liquid equilibrium model has been formulated to predict the trapping efficiency for extracted solute collection in a open collection vessel. Secondly, a simple solution thermodynamic model for predicting solute (analyte) activity coefficients in various trapping solvents has been shown to have utility in predicting collection efficiencies. Finally, effective trapping efficiency after SFE using sorbent media is related to the extent of analyte breakthrough on the sorbent-filled trap after depressurization of supercritical fluid. Using experimental data determined via physico-chemical gas chromatographic measurements (i. e., specific retention volumes), we have shown the relationship between analyte breakthrough volume off of the trapping sorbent and volume of depressurized fluid through the collection trap. The above theoretical guidlines should prove of value to analysts in designing and optimizing the best conditions for trapping analytes after extraction via analytical SFE. Names are necessary to report factually on available data; however the USDA neither guarantees nor warrants the standard of the product, and the use of the name by USDA implies no approval of the products to the exclusion of others that may also be suitable.     

The Slope of Roofs

We consider a dome, modeled as a thin membrane of revolution, closed at the vertex and of unit height as shown in Figure 1. The membrane is loaded at the vertex by a force P, perpendicular to the axis of symmetry, and constrained at the base so that only membrane forces are transmitted to the ground. On assuming that the shape of the meridian is unknown, but the total volume, product of the surface area for the thickness, is given, we try to find the profile of the meridian which minimizes the highest stress at the base. It cannot yet be shown that the full problem admits a solution. However, comparison between some particular cases may give an idea of what the best shape of the dome must be.     

简谐振子免疫优化算法求解异构无线网络垂直切换判决问题

本文设计了垂直切换判决方案问题的数学模型, 给出了一种基于简谐振子免疫优化算法的垂直切换判决方案, 并与文献方案进行了对比实验实验结果表明, 本文方案能够有效地平衡网络负载、增加终端电池的生存时间, 具有较好的应用价值.     

Convergence of an annealing algorithm

The annealing algorithm is a stochastic optimization method which has attracted attention because of its success with certain difficult problems, including NP-hard combinatorial problems such as the travelling salesman, Steiner trees and others. There is an appealing physical analogy for its operation, but a more formal model seems desirable. In this paper we present such a model and prove that the algorithm converges with probability arbitrarily close to 1. We also show that there are cases where convergence takes exponentially long—that is, it is no better than a deterministic method. We study how the convergence rate is affected by the form of the problem. Finally we describe a version of the algorithm that terminates in polynomial time and allows a good deal of practical confidence in the solution.     

Modeling Very Oscillating Signals. Application to Image Processing

This article is a companion paper of a previous work where we have developed the numerical analysis of a variational model first introduced by Rudin et al. and revisited by Meyer for removing the noise and capturing textures in an image. The basic idea in this model is to decompose an image f into two components (u + v) and then to search for (u,v) as a minimizer of an energy functional. The first component u belongs to BV and contains geometrical information, while the second one v is sought in a space G which contains signals with large oscillations, i.e. noise and textures. In Meyer carried out his study in the whole ² and his approach is rather built on harmonic analysis tools. We place ourselves in the case of a bounded set of ² which is the proper setting for image processing and our approach is based upon functional analysis arguments. We define in this context the space G, give some of its properties, and then study in this continuous setting the energy functional which allows us to recover the components u and v. We present some numerical experiments to show the relevance of the model for image decomposition and for image denoising.     

LED阵列模组化中的照度均匀性问题

为使LED阵列在目标面上产生均匀的照度分布,构建一个均匀度评价函数.通过优化阵列中各LED的位置,使得评价函数产生最小值,此时阵列在照明面上的均匀度最高.采用模拟退火算法对LED集成圆形阵列、4×4和4×5的矩形阵列进行优化,优化后的阵列在目标面上均产生了均匀照度分布,对应的均匀度分别为97%、96%和95%.对LED阵列中影响目标面均匀照度的参量进行研究,结果表明圆形阵列的最佳半径与LED的颗数无关,阵列面和目标面之间的距离与阵列的最佳半径呈线性关系;矩形阵列中,相邻LED之间的最佳距离与阵列面和目标面之间的距离呈线性关系.该方法与现有解析方法所得结果一致,且算法简单便捷,可用于计算机自动优化设计.     

一种数字微镜阵列分区控制和超分辨重建的压缩感知成像法

提出一种压缩感知成像框架结构.该结构采样端用新建的采样矩阵实现数字微镜阵列分区控制,可增强信息获取的准确性,测量得到与新数字微镜阵列对应的压缩采样值;重构端由采样值优化重构出低分辨率图像后,根据分区控制过程建立压缩感知理论框架下的超分辨重建模型,利用梯度稀疏约束优化算法进行求解,恢复出原高分辨率图像.实验结果表明:数字微镜阵列分区控制与超分辨重建相结合的方法可以明显降低压缩感知成像系统的计算量,缩短成像时间,并且具有较高的图像重构质量.     

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

The acoustic characteristics of cries are an exhibition of an infant’s health condition and these characteristics have been acknowledged as indicators for various pathologies. This study focused on the detection of infants suffering from sepsis by developing a simplified design using acoustic features and conventional classifiers. The features for the proposed framework were Mel-frequency Cepstral Coefficients (MFCC), Spectral Entropy Cepstral Coefficients (SENCC) and Spectral Centroid Cepstral Coefficients (SCCC), which were classified through K-nearest Neighborhood (KNN) and Support Vector Machine (SVM) classification methods. The performance of the different combinations of the feature sets was also evaluated based on several measures such as accuracy, F1-score and Matthews Correlation Coefficient (MCC). Bayesian Hyperparameter Optimization (BHPO) was employed to tailor the classifiers uniquely to fit each experiment. The proposed methodology was tested on two datasets of expiratory cries (EXP) and voiced inspiratory cries (INSV). The highest accuracy and F-score were 89.99% and 89.70%, respectively. This framework also implemented a novel feature selection method based on Fuzzy Entropy (FE) as a final experiment. By employing FE, the number of features was reduced by more than 40%, whereas the evaluation measures were not hindered for the EXP dataset and were even enhanced for the INSV dataset. Therefore, it was deduced through these experiments that an entropy-based framework is successful for identifying sepsis in neonates and has the advantage of achieving high performance with conventional machine learning (ML) approaches, which makes it a reliable means for the early diagnosis of sepsis in deprived areas of the world.