基于波束形成的多类型多声源定位研究

为实现空压机多噪声源的准确定位,仿真对比了多种近场球面波多声源定位算法。基于时域波束形成,研究了相同声源平面、不同声源频率、不同声源纵向距离、不同声源强度下多声源定位以及声源频率、声源纵向距离和声源强度多因素联合的多声源定位仿真方法,模拟了更接近实际的噪声源类型。基于频域波束形成,仿真研究了1400 Hz,2400 Hz,3400 Hz,4400 Hz的多声源。分别利用互功率谱波束形成和除自谱的互功率谱波束形成,仿真研究了相干声源和不相干声源。开发了阵列声成像测试平台,运用频域波束形成和功率谱波束形成对空压机进行了定位试验研究。结果表明,1400 Hz下空压机的主要噪声源是气缸盖、空气滤清器和曲轴附近的机体,这可为空压机减振降噪改进设计提供依据。     

40 Gbps OFDM-PON系统采用偏振复用技术实现上行链路传输(英文)

为了提高PM-CO-OFDM-PON系统中上行数据的传输速率,提出采用偏振复用结构与相干检测技术相结合,通过光载波源方式实现上行链路光网络单元无色化传输的技术方案.利用光学软件VPI和Matlab,搭建了基于偏振复用技术的40Gb/s PM-CO-OFDM-PON系统仿真平台,结果表明:该方案可有效提高PM-CO-OFDM-PON系统中上行数据传输速率,并实现光网络单元无色化;利用相干检测比直接检测可以更高地提高接收端的灵敏度.     

利用可见近红外光谱多指标综合预测生鲜牛肉储存期

建立了牛肉基于TVB-N、菌落总数、pH值和肉色参数L*多个指标的储存期预测模型,利用可见近红外光谱(Vis/NIR)技术结合区间偏最小二乘(iPLS)和遗传算法(GA)建立了各个指标的PLS预测模型,实现了多指标综合无损快速预测4 ℃下牛肉的储存期。用iPLS和iPLS-GA提取有效波长变量建立PLS预测模型,以预测相关系数和预测标准差作为模型评价标准,结果表明用iPLS-GA选择变量建立的各个指标的PLS预测模型均优于全波段和iPLS组合的PLS模型。由多个指标的预测值和储存期的预测模型,对校正集和预测集样品储存期进行预测,其预测相关系数和标准差分别是0.903, 0.897和1.88, 2.24。说明利用光谱技术结合得出的储存期预测模型可以实现多指标综合预测牛肉储存期,为无损快速检测牛肉储存期或货架期提供了一种新方法。      

Asymmetric phase-transfer catalysts bearing multiple hydrogen-bonding donors: Synthesis and application in nitro-Mannich reaction of isatin-derived N-Boc ketimines

A series of bifunctional asymmetric phase-transfer catalysts bearing multiple hydrogen-bonding donors derived from cinchona alkaloids are synthesized, and successfully applied to asymmetric nitro-Mannich of isatin-derived N-Boc ketimines. The products 3-substituted 3-amino-oxindoles were constructed in excellent yields (96–99%) and good enantioselectivities (up to 95% ee).     

A multi-parametric programming approach for multilevel hierarchical and decentralised optimisation problems

In this paper, we outline the foundations of a general global optimisation strategy for the solution of multilevel hierarchical and general decentralised multilevel problems, based on our recent developments on multi-parametric programming and control theory. The core idea is to recast each optimisation subproblem, present in the hierarchy, as a multi-parametric programming problem, with parameters being the optimisation variables belonging to the remaining subproblems. This then transforms the multilevel problem into single-level linear/convex optimisation problems. For decentralised systems, where more than one optimisation problem is present at each level of the hierarchy, Nash equilibrium is considered. A three person dynamic optimisation problem is presented to illustrate the mathematical developments.     

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.     

General synthesis of mono-, di-, and tri-acetylated indoles from indolin-2-ones

Having developed the one-pot triacetylation of indolin-3-ones, we have now devised a simple two-step reaction sequences to produce di- and mono-acetylated indoles from indolin-2-ones. The indolin-2-ones were first subjected to acetylation in the presence of acetic anhydride and a catalytic amount of N,N-dimethylaminopyridine to give 2-acetoxy-1,3-diacetylindoles. Subsequently, an enzyme-assisted deacetylation resulted in the chemoselective deprotection of the acetoxy group to produce 1,3-diacetyl-2-hydroxyindoles. However, a chemical deacetylation of 2-acetoxy-1,3-diacetylinoles under mild basic or acidic conditions resulted in the formation of 3-acetyl-2-hydroxyindoles.     

A practical weight sensitivity algorithm for goal and multiple objective programming

This paper presents a weight sensitivity algorithm that can be used to investigate a portion of weight space of interest to the decision maker in a goal or multiple objective programme. The preferential information required from the decision maker is an initial estimate of their starting solution, with an equal weights solution being used as a default if this is not available, and preference information that will define the portion of weight space on which the sensitivity analysis is to be conducted. The different types of preferential information and how they are incorporated by the algorithm are discussed. The output of the algorithm is a set of distinct solutions that characterise the portion of weight space searched. The possible different output requirements of decision makers are detailed in the context of the algorithm.The methodology is demonstrated on two examples, one hypothetical and the other relating to predicting cinema-going behaviour. Conclusions and avenues for future research are given.     

决策树分类模型在工程项目评标风险预警中的应用

本文将数据挖掘中的决策树分类方法运用到工程项目评标数据分析,从200多个天津市工程项目招投标打分数据中,随机抽取15个招投标项目中的67个承包商的评标专家打分数据进行分析,得到中标承包商技术和商务评分分界点,进而得到工程项目潜在风险的预警阈值,然后借助因子分析辨识出风险来源并进行预警。     

Relaxation of Nonlocal Singular Integrals

In this paper we study well-posedness of a class of nonconvex variational principles arising in regularization theory for denoising of data with sampling errors and level set regularization methods for inverse problems. These models result in minimization of nonconvex, singular functionals involving (possibly) non-local operators.