基于多元统计理论的干部评价模型与实证分析

以多元统计分析原理为依据，运用聚类分析、主成分分析、碎石检验准则对高校党政管理干部做出综合评价及实证分析，可以得出干部的分类和综合排序情况，为高校组织部门和人事部门考核、选拔、任用干部提供科学的测评依据。     

无纺土工织物在堤防抢险中的应用

阐述了土工织物在堤防管护中的应用方法、施工注意事项以及无纺土工织物的选用原则。     

Application of Kernel-Based Feature Space Transformations and Learning Methods to Phoneme Classification

This paper examines the applicability of some learning techniques to the classification of phonemes. The methods tested were artificial neural nets (ANN), support vector machines (SVM) and Gaussian mixture modeling (GMM). We compare these methods with a traditional hidden Markov phoneme model (HMM), working with the linear prediction-based cepstral coefficient features (LPCC). We also tried to combine the learners with linear/nonlinear and unsupervised/supervised feature space transformation methods such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), springy discriminant analysis (SDA) and their nonlinear kernel-based counterparts. We found that the discriminative learners can attain the efficiency of HMM, and that after the transformations they can retain the same performance in spite of the severe dimension reduction. The kernel-based transformations brought only marginal improvements compared to their linear counterparts.     

信息融合中特征信息提取方法研究与应用

分析了信息融合的特点,提出信息融合的关键在于特征层的融合。采用主成分分析法、粗集理论分别对综合传动装置道路试验油液污染分析的结果进行特征信息提取,找出影响综合传动污染状况的主要污染颗粒尺寸,再用相关分析法,找出各特征信息之间的相关性,并用于污染状况判断。试验证明,这两种方法都可用于特征信息的提取,并且取得了较一致的结果,而相关分析法与上述两种方法具有较好的互补性。将研究成果应用于综合传动装置污染控制,有效地防止了由于污染所造成的故障发生。     

基于稀疏分解和背景差分融合方法的图像处理技术

针对传统融合算法处理聚焦区域能力弱、边缘效果差以及目标轮廓提取存在缺陷等问题,提出了基于稀疏分解和背景差分融合的方法.稀疏分解经过鲁棒主成分分析方式提取多聚焦图像轮廓,从而为源图像的准确分离奠定基础;背景差分融合依照源图像与增强图像的差异图提取轮廓信息以准确定位聚焦区域,从而防止引入人工干扰.结果表明,与传统方法相比,本文提出的方法在很大程度上提升了融合效果,能够很好地加强其对噪声的鲁棒性,同时表现出很好的视觉效果.     

MIMO SAR OFDM chirp waveform design and GMTI with RPCA based method

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.     

利用测井资料分析计算东海平湖油气田地应力

利用测井资料研究平湖油气田的地应力,对平湖油气田低渗透储层压裂、钻井和开发都有重要意义。文中利用密度、自然伽马、交叉偶极横波测井等资料,对平湖油气田地层的岩石力学参数(泊松比、弹性模量、体积模量、剪切模量、抗压强度、抗剪强度和抗张强度)进行了计算;为确定地层压力和构造应力系数,借鉴前人的研究成果,分别应用黄氏和葛氏地应力计算模型,建立平湖油气田地应力计算方法,并通过岩心差应变法地应力实验验证了该方法的准确性;采用基于XMAC-Ⅱ快横波方位法确定的平湖油气田的地应力方向,与岩心波速各向异性法及黏滞剩磁法地应力实验确定的地应力方向吻合较好。     

Geographical characterisation of honeys according to their mineral content and antioxidant activity using a chemometric approach

In this article, discrimination models are presented, relating the origin of honey samples to several variables, being the concentrations of different cations and anions in the honey samples measured by ion chromatography, and parameters that measure/reflect the antioxidant activity of the honey samples. The unsupervised method, principal component analysis, and supervised discrimination methods, such as linear and quadratic discriminant analysis, and classification and regression trees (CART), were applied to evaluate the existence of data patterns and the relationship between geographical origin and the measured parameters. The model with the best predictive ability (%CCR_TEST = 66.67%), the best overall % specificity (80%) and the best overall % sensitivity (67%) was found to be CART. It was proven that the mineral content and parameters analysed can provide enough information for the geographical characterisation and discrimination of honey.     

Real-Time Assessment and Diagnosis of Process Operating Performance

Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis (MclustDA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression (MPPCR) method, and dynamic principal component regression (DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search (SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman (TE) benchmark simulation process in order to evaluate its performance.     

Edible mushrooms: a potential source of essential amino acids,glucans and minerals

The main chemical and biochemical constituents of nine edible mushrooms were evaluated in this study. An enzymatic method was used to determine the α‐ and β‐glucan contents, and the amino acid (AA) profiles were evaluated by RP‐HPLC‐DAD. The recommended dietary allowance (RDA) of the nutrients was evaluated. Finally, principal component analysis (PCA) was used to group the mushrooms according to their AA, glucans and chemical composition. All mushrooms evaluated can be considered as a potential and alternative source of dietary fibre (24.4–46.62%) and protein (16.47–36.96%) in a diet, with low contents of fat (1.40–2.08%). Phosphorus, potassium, iron, copper and zinc were the main minerals found in the mushrooms, whereas sodium concentration was negligible. All essential AA were detected in the mushrooms, with Pleurotus ostreatus (black oyster) representing the main source of essential amino acids among the samples. The contents of β‐glucan ranged from 1.58 to 16.91 mg g^?1 of dry matter among the mushrooms and Pleurotus eryngii presented the highest amount of this component. Pleurotus ostreatus (black oyster) and P. eryngii were also grouped together due to similarities in their phosphorous and arginine contents. Among the main results, it can highlight the high concentration of dietary fibre and essential amino acids of the edible mushrooms.