土壤养分有效性测定及其方法

在提出土壤养分有效性测定概念的基础上 ,本文对各种土壤养分有效性的测定方法进行了总结 ,讨论了这些方法的测定机理、测定效果及近几年的进展。这些方法包括用于磷钾等元素测定的树脂法、用于氮测定的生物培养法和化学提取法、磷测定的氧化铁试纸法和氢氧化铁透析管法和钾的四苯硼钠法     

ISE_s测定大黄素对兔尿中钠、钾排泄的影响

ISE_s(离子选择电极)作为一项新技术,在六十年代初期崭露头角,并迅速在临床化学和生理医学研究中获得应用。本文应用ISE_s法进行动物药理试验的初步尝试,取得了满意结果。中药大黄具有清热利尿作用,口服大黄生药或大黄素,尿中钠、钾均有不同程度的变化。钠、钾常用测定方法有火焰光度法、原子吸收法、比色法和ISE_s法等。前两种方法需要特殊的仪器,比色法将钠钾萃取出来,操作烦琐、费时。本文研究以0.1M三乙醇     

谈对丹参制备工艺技术的分析

目的：探讨冷浸法提取丹参脂溶性部位的最佳工艺条件。方法：采用高效液相色谱（HPLC）法为测定方法,以丹参酮ⅡA提取量为指标,考察不同浓度乙醇和粉碎对丹参酮ⅡA提取率的影响,并用正交试验法进一步优选提取条件。结果：最佳提取工艺条件为采用8倍量体积分数95％乙醇冷浸提取2次（每次3h）,在这种条件下,可获得较高的丹参酮ⅡA提取率。结论：此工艺条件简单、稳定,提取率高。     

基于PCA-BP的中药提取率的软测量

利用超声波萃取金银花中的绿原酸.依据工艺机理和操作经验,从提取过程中初选了数学模型所需的输入变量.对萃取过程中的提取率,利用主元分析方法PCA对输入变量进行主元分解,消除了输入变量之间的线性相关性,再利用BP神经网络进行数学建模,仿真结果表明:利用PCA法对输人数据进行处理后,与单独使用BP算法的建模结果相比较,该方法具有较快的训练速率和较高预测精度,提高了对中药萃取过程中提取率的在线软测量精度.     

高精度臭灵丹微量元素含量检测系统设计

为了实现快速、无损、实时检测和分析臭灵丹中微量元素的含量,现对高精度臭灵丹微量元素检测系统进行设计。使用当前方法检测臭灵丹时,无法在保证微量元素活性的条件下提取微量元素。为此,提出一种基于Taguchi的高精度臭灵丹微量元素含量检测系统设计方法。该方法使用日本理学ZSX100型X射线荧光光谱仪采集臭灵丹微量元素光谱,确定微量元素种类,利用超临界萃取技术中的两大技术超临界流体萃取技术和超临界固体萃取技术萃取出臭灵丹中的微量元素,再经过超滤分离法中直线段、曲线段、水平段三个阶段分离出萃取液中微量元素,以ICP-MS法计算出微量元素的含量,达到对臭灵丹中微量元素含量的高精度检测。实验仿真证明,所提方法可以快速、无损、实时检测和分析臭灵丹中微量元素。     

土壤微生物生物量熏蒸提取法中转换系数KEC的测定研究

本文综述了熏蒸提取法中用于计算土壤微生物生物量时所需的转换系数KEC的测定方法 ,并对现有的各种方法的应用性作了简要的评价 ;同时指出了目前常采用的KEC值在使用上应注意的事项     

用萃取和结晶法提取茄尼醇的研究

选择含量为35%左右的茄尼醇粗品为原料,采用萃取和结晶法提取茄尼醇,探讨萃取和结晶剂的用量、温度对萃取和结晶过程的影响。采用最小方差法关联实验数据、牛顿法迭代,实验及关联结果:在萃取和结晶中,将5.5g茄尼醇溶于18ml乙酸乙酯中,加40ml乙腈,控制温度为66℃,可获得纯度较高的产品,如茄尼醇的纯度为62%,则收率超过65%。     

分子标志物参数在识别土壤多环芳烃(PAHs)来源中的应用

土壤中PAHs污染物的成因十分复杂,常见的污染源包括生物质的高温降解产物、石油等化石燃料及其不完全燃烧产物等,其输入方式主要有大气中所含PAHs的干、湿沉降、水体输入、固体废弁物排放等.不同成因的PAHs组成特征有一定差别,并可能具有独特的分子标志物或分子化合物组合特征,由此,可以根据环境介质中PAHs的组成特征判断污染物来源或成因类型.目前,分子标志物特征参数已成为追踪PAHs污染来源的有效手段.介绍了近年来国内外在运用PAHs分子标志物特征参数识别土壤中PAHs污染源方面的主要研究进展、应用潜力及存在的问题.     

采用改进的遗传算法与Mod.UNIFAC基团贡献设计萃取剂

将Mod．UNIFAC基团贡献法与改进的遗传算法相结合,进行萃取精馏萃取剂的分子设计。针对遗传算法搜索效率低和不易得到全局最优解等问题,对遗传算法的编码方案和遗传操作算子中的选择和变异算子进行了改进,有效避免了该方法的自身缺陷。将其应用于乙醇-乙酸乙酯体系,设计得到了三甲苯、丙三醇、氯苯等高效萃取剂,将相对挥发度的预测值与文献值比较,平均偏差小于8%。说明了所采用的方法是准确可靠的。     

用TPB电极测定RE（TMSO）8（TPB）3中的TPB含量

TPB 为四苯硼酸根的缩写,TPB 的测定方法有重量法、萃取滴定法和孔雀绿为显色剂的光度法等。TPB 电极作为滴定剂电极的研究较多,但尚无用于 TPB 分析的报导。本文以 DTOA-TPB 为活性材料,以 DBP 一氯苯为溶剂制成 PVC 膜 TPB 电极,避免了文献中所用的毒性较大的 DBP 一硝基苯溶剂。并用该电极为指示电极,N—CPC 为滴定剂,成功地测定了环丁基亚砜四苯硼稀土络合物中的四苯硼酸根含量。     

Pattern representation in feature extraction and classifier design: matrix versus vector.

The matrix, as an extended pattern representation to the vector, has proven to be effective in feature extraction. However, the subsequent classifier following the matrix-pattern- oriented feature extraction is generally still based on the vector pattern representation (namely, MatFE + VecCD), where it has been demonstrated that the effectiveness in classification just attributes to the matrix representation in feature extraction. This paper looks at the possibility of applying the matrix pattern representation to both feature extraction and classifier design. To this end, we propose a so-called fully matrixized approach, i.e., the matrix-pattern-oriented feature extraction followed by the matrix-pattern-oriented classifier design (MatFE + MatCD). To more comprehensively validate MatFE + MatCD, we further consider all the possible combinations of feature extraction (FE) and classifier design (CD) on the basis of patterns represented by matrix and vector respectively, i.e., MatFE + MatCD, MatFE + VecCD, just the matrix-pattern-oriented classifier design (MatCD), the vector-pattern-oriented feature extraction followed by the matrix-pattern-oriented classifier design (VecFE + MatCD), the vector-pattern-oriented feature extraction followed by the vector-pattern-oriented classifier design (VecFE + VecCD) and just the vector-pattern-oriented classifier design (VecCD). The experiments on the combinations have shown the following: 1) the designed fully matrixized approach (MatFE + MatCD) has an effective and efficient performance on those patterns with the prior structural knowledge such as images; and 2) the matrix gives us an alternative feasible pattern representation in feature extraction and classifier designs, and meanwhile provides a necessary validation for "ugly duckling" and "no free lunch" theorems.     

特征提取及其在电子鼻对可燃液体识别中的应用

利用6只TGS传感器组成的阵列对4种常见的易燃液体和3种不可燃饮料进行测试,并选用4种有代表性的特征提取方法,主元分析法(PCA)、Fisher判别法(FDA)、自组织映射(SOM)、Sammon映射法(Sammon map)作为数据预处理方法,并用3种模式识别方法对预处理后的数据进行识别。结果表明:在各种特征提取方法的处理下,可燃类和不可燃类样本都能被准确地区分,而只有在有导师的特征提取方法才能有效地识别各个可燃液体类子类和不可燃液体类子类的样本类别,最佳的投影维数与各特征提取方法有密切联系,而最优的模式识别方法则与数据的分布有关。     

医学图像中微细管道结构的提取

以肝动脉提取为例,提出一种医学图像中微细管道结构的提取算法。算法主要步骤包括：最大亮度投影、二维管道提取、三维种子点生成和三维管道提取。算法对常规的区域生长法进行改进,提取效果与常规的区域生长法相比有较大改善。算法需要的人工干预很少,只需要指定四个参数。     

Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.     

Automatic recognition of multi-word terms:. the C-value/NC-value method

Technical terms (henceforth called terms ), are important elements for digital libraries. In this paper we present a domain-independent method for the automatic extraction of multi-word terms, from machine-readable special language corpora. The method, (C-value/NC-value ), combines linguistic and statistical information. The first part, C-value, enhances the common statistical measure of frequency of occurrence for term extraction, making it sensitive to a particular type of multi-word terms, the nested terms. The second part, NC-value, gives: 1) a method for the extraction of term context words (words that tend to appear with terms); 2) the incorporation of information from term context words to the extraction of terms. Received: 17 December 1998 / Revised: 19 May 1999     

反编译器中针对不同编译器的过程识别技术

过程识别技术及相关参数的提取是二进制翻译中过程调用恢复的基础.为较好实现对过程的识别,首先设计了针对GCC编译的ELF(executable and linkable format)文件的过程识别技术,取得了良好的效果.不过随着研究的深入,要求对C编译器和ICC(Intel C compiler)编译器同时具有良好的支持,但在测试中发现这种识别技术在处理ICC编译的ELF程序指令流时存在的一些问题,为此提出了改进算法,这个算法已经在IA-64-Alpha反编译中实现,从而使系统对C编译器和ICC编译器编译的ELF文件都能进行正确的过程识别和参数提取.     

面向多标签文本分类的深度主题特征提取

针对单标签特征提取方法不能有效解决多标签文本分类的问题,文中提出融合主题模型(LDA)与长短时记忆网络(LSTM)的双通道深度主题特征提取模型(DTFEM).LDA与LSTM分别作为两个通道,通过LDA为文本的全局特征建模,利用LSTM为文本的局部特征建模,使模型能同时表达文本的全局特征和局部特征,实现有监督学习与无监督学习的有效结合,得到文本不同层次的特征提取.实验表明,相比文本特征提取模型,文中模型在多标签分类结果上的多项指标均有明显提升.     

Brain tumor segmentation and classification in MRI using SVM and its variants: a survey

The process of separation of brain tumor from normal brain tissues is Brain tumor segmentation. Segmentation of tumor from the MR images is a very challenging task as brain tumors are of different shapes and sizes. There are multiple phases to achieve the segmentation and the phases are pre-processing, segmentation, feature extraction, feature reduction, and classification of the tumor into benign and malignant. In this paper, Otsu thresholding is used in segmentation phase, Discrete Wavelet Transform (DWT) in feature extraction phase, Principal Component Analysis (PCA) in feature reduction phase and Support Vector Machine (SVM), Least Squared-Support Vector Machine (LS-SVM), Proximal Support Vector Machine (PSVM) and Twin Support Vector Machine (TWSVM) in the classification phase. We have compared the performances of all these classifiers, where TWSVM outperformed all other classifiers with 100% accuracy.

     

Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

The purpose of this study was to explore the value of extraction of tumor features in contrast-enhanced ultrasonography (CEUS) images based on the deep belief networks (DBN) for the diagnosis of cervical cancer patients and realize the intelligent evaluation on effects of diagnosis and chemotherapy of the cervical cancer. An automatic extraction algorithm with the time-intensity curve (TIC) was proposed based on Sparse nonnegative matrix factorization (SNMF) in this study, and was applied to the framework of automatic analysis of cervical cancer tumors based on the deep belief networks, to assist doctors in the analysis of cervical cancer tumors. The framework was applied to the real clinical diagnostic data, and the feasibility of the method was verified by comparing the accuracy, sensitivity, and specificity. Later, the parameters of patients’ time to peak (TP), peak intensity (PI), mean transit time (MTT), and area under the curve (AUC) were obtained by drawing TICs, and the changes of p53 protein and ki-67 protein obtained by pathological section staining were analyzed to evaluate the therapeutic effect in the patients. It was found that the proposed model of tumor feature extraction based on the DBN had the higher accuracy (86.36%), sensitivity (83.33%), and specificity (87.50%). The related parameters of TIC curve obtained based on SNMF showed that there was a significant difference in p53 content between tissues with different degrees of disease (p?<?0.05), the PI of poorly differentiated tissues was significantly higher than that of those with high to medium differentiation (p?<?0.05). In addition, PI and AUC of patients after chemotherapy were significantly lower than that before chemotherapy (p?<?0.05), while MTT was significantly higher than that before chemotherapy (p?<?0.05). Therefore, the proposed TIC feature extraction of CEUS images based on SNMF and the automatic tumor classification based on deep learning can be used in the diagnosis and efficacy evaluation of cervical cancer patients.

     

一种多策略联合信息抽取方法

介绍了一种多策略联合信息抽取方法——MSCIE(Multi-Strategy Comtbination Information Extraction)．MSCIE将对表格式网页的信息抽取分为基于网页结构特征分析的信息抽取和基于模式匹配的信息抽取，提出了一种对网页DoM(Document Object Moclel)树的冗余信息进行剪枝分析的方法和一种实体特征模式发现算法分别用于这两种信息抽取方法，并通过两种策略联合完成信息抽取工作．应用于互联网竞争情报监测系统中，从大量网站中抽取多种商品的供求信息，取得了较高的准确率和召回率(平均在95％以上)。