模糊一致矩阵理论在地铁深基坑支护方案优选中的应用

根据地铁深基坑支护工程的特点,建立了支护方案评价的指标体系,并将模糊数学理论与层次分析法结合起来,构造出地铁深基坑支护方案优选的模糊一致矩阵数学模型。该模型概念清晰、理论严密、计算简便、适用范围广且评价结果客观可靠。实例分析表明,用该方法进行深基坑支护方案优选是可行的、有效的。与其它方法相比,该方法可灵活处理各种复杂的决策问题,易于编制计算机程序。     

Fisher判别分类法在光伏并网系统稳态电能质量评估中的应用

针对稳态电能质量综合评估的复杂性以及单项指标在反映电能质量综合水平的局限性,首次提出将Fisher判别分类法用于并网光伏稳态电能质量的评估,进而确定光伏并网可行性。根据已知的电能质量国家标准,通过构造样本来得到判别模型,然后利用回代估计法对模型进行检验,并对实际系统进行判别分析。实验结果表明所提方法所得误判率小,具有较强的判别能力。     

基于贝叶斯决策模型的热轧卷筒电机故障诊断

卷筒电机是卷筒的动力来源,对钢卷卷形质量和卷取稳定性有着重要影响。针对热连轧机组卷取工序中卷筒电机的鼠笼条断裂问题,通过从现场采集卷筒电机电流信号,并经过信号截取、滤波、拟合等操作提取出信号特征,利用多元高斯模型进行建模,并基于最小风险的贝叶斯分类算法对其进行故障诊断。试验结果表明,采用最小风险贝叶斯决策模型可以较好地识别卷筒电机的鼠笼条断裂故障,与支持向量机、Fisher等分类器相比,该方法具有较好的分类效果,且通过调节风险系数能够增强对异常样本的捕获能力。     

Efficient loading and entrapment of tamoxifen in human serum albumin based nanoparticulate delivery system by a modified desolvation technique

Herein, the poorly water-soluble drug, Tamoxifen (Tmx), was loaded in the amphipathic matrix of human serum albumin (HSA) nanoparticles by a modified desolvation method. In order to enhance the drug loading (DL) and drug entrapment efficiency (DEE) (<2% and 10%, respectively), ultrasonication of Tmx-HSA mixture was performed prior to desolvation process. Tmx loading and entrapment efficiency were optimized by employment of the response surface methodology (RSM)-central composite design (CCD) of experiments. Under the optimum conditions of 1.59 mg Tmx/ml concentration, 7.76 pH and 5 h incubation of HSA-Tmx, the DL of 6.7% and DEE of 74% are achievable. Particles with the average size of 195 nm, zeta potential of −21 mV and polydispersity index of 0.09 were produced under these conditions. A more sustained Tmx release behavior was observed from polyethylene glycol (PEG) conjugated nanoparticles in comparison to the non-PEGylated ones. The short-term stability investigation showed no alteration in physicochemical properties of nanoparticles at 4 and 37 °C, but small increase in nanoparticles size was observed after three months of storage at room temperature. This is the first report for efficient production of a Tmx delivery system based on HSA nanoparticles.     

A multi-model approach for soft sensor development based on feature extraction using weighted kernel fisher criterion简

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.     

Technological Innovation: Regaining the Competitive Edge

ABSTRACT

Global competition has become a business reality. To become competitive, we must improve the rate at which new technical concepts are incorporated into our products and processes. This article describes the sources and driving forces of innovation and the creative environment that the engineering manager must consider to increase the innovative productivity of the organization. Managers must also recognize the impact that the technical education system has on future innovative productivity and take the initiative to improve it. They must emphasize the importance of systems and process research and support such programs. In addition, idea generation must be considered just as important as analysis to the long-term success of the technological enterprise.     

Designing experiments to understand the variability in biochemical reaction networks

Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori, to decide whether a single-cell experiment might be beneficial, is currently only possible in systems where either the chemical master equation is computationally tractable or a Gaussian approximation is appropriate. Here, we provide formulae for computing the information provided by measured means and variances from the first four moments and the parameter derivatives of the first two moments of the underlying process. For stochastic kinetic models for which these moments can be either computed exactly or approximated efficiently, the derived formulae can be used to approximate the information provided by single-cell distribution experiments. Based on this result, we propose an optimal experimental design framework which we employ to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression. Subsequently, we compare the information content of a set of experiments which have been performed in an engineered light-switch gene expression system in yeast and show that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.     

Use of a choice-based survey approach to characterise fishing behaviour in a scallop fishery

The predictability of fisher behaviour is an area of considerable uncertainty in fisheries management models. Fisher-derived data could underpin a better understanding, and more realistic predictions of fishing behaviour. Face to face interviews and a choice-based survey were conducted with scallop fishers to collect foraging parameters that could inform a model of fishing behaviour, and to better understand patch choice behaviour. Importantly, we validated survey data against vessel monitoring system and logbook data where possible, demonstrating a good level of accuracy. Environmental parameters central to patch choice were determined (e.g. wave height, distance to port), and three strategies of patch choice behaviour were identified, termed quantity maximiser, quality maximiser, and efficient fisher. Individuals' VMS and logbook data further confirmed and explained these behavioural patterns. This approach provided reliable, highly relevant data for the parameterisation of a fisheries behavioural model, which could lead to more robust and realistic predictive fisheries models.     

Study on defect classification in multi-layer structures based on Fisher linear discriminate analysis by using pulsed eddy current technique

Pulsed eddy current (PEC) is an emerging non-destructive testing technique with wide application potential. In this study, defect parameter identification in multi-layer structures is examined by using the PEC technique, and a Fisher linear discriminate analysis (FLDA)-based defect classification method is proposed. Defect localization and shape identification are investigated, and defects on the surface and subsurface of the third layer are discriminated. The time domain characterization method is introduced and researched by using the peak time and zero-crossing time of PEC response signals, the principal component analysis algorithm and the FLDA method. The feature extraction results of the three methods are used as the input values of support vector machine for defect classification and feature extraction, and the classification methods are compared. Theoretical analysis and experimental results show that the proposed method can contribute to effective classification for defect parameter identification.     

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way.The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.