Space-resolved spectral diagnosis of line-shaped laser-produced magnesium plasmas

Space-resolved spectra of line-shaped laser-produced magnesium plasmas in the normal direction of the target have been obtained using a pinhole crystal spectrograph. These spectra are treated by a spectrum analyzing code for obtaining the true spectra and fine structures of overlapped lines. The spatial distributions of electron temperature and density along the normal direction of the target surface have been obtained with different spectral diagnostic techniques. Especially, the electron density plateaus beyond the critical surface in line-shaped magnesium plasmas have been obtained with a fitting technique applied to the Stark-broadened Ly-alpha wings of hydrogenic ions. The difference of plasma parameters between those obtained by different diagnostic techniques is discussed. Other phenomena, such as plasma satellites, population inversion, etc., which are observed in magnesium plasmas, are also presented.

Diagnosis of colorectal cancer using Raman spectroscopy of laser-trapped single living epithelial cells

A single-cell diagnostic technique for epithelial cancers is developed by utilizing laser trapping and Raman spectroscopy to differentiate cancerous and normal epithelial cells. Single-cell suspensions were prepared from surgically removed human colorectal tissues following standard primary culture protocols and examined in a near-infrared laser-trapping Raman spectroscopy system, where living epithelial cells were investigated one by one. A diagnostic model was built on the spectral data obtained from 8 patients and validated by the data from 2 new patients. Our technique has potential applications from epithelial cancer diagnosis to the study of cell dynamics of carcinogenesis. (c) 2006 Optical Society of America.

Revised diagnosis of the genus Bangana Hamilton, 1822 (Pisces : Cyprinidae), with taxonomic and nomenclatural notes on the Chinese species

Most morphological characters diagnostic of the 13 Chinese species of the cyprinid genus Sinilabeo Rendahl, 1932, are identical to those of the genus Bangana Hamilton, 1822. Consequently, these 13 species are transferred to Bangana. A revised diagnosis is provided for the now-expanded genus Bangana, and a dichotomous key and taxonomic and nomenclatural notes are included for the following valid Chinese species: B. decora, B. dero, B. devdevi, B. discognathoides, B. lemassoni, B. lippa, B. rendahli, B. tonkinensis, B. tungting, B. wui, B. xanthogenys, B. yunnanensis, and B. zhui. Literature reports, by Chinese authors, of Sinilabeo dero from the upper Irrawady River basin, in Yunnan, are based on misidentifcations of B. devdevi. Sinilabeo cirrhinoides Wu and Lin in Wu, Lin, Chen, Chen and He, 1977, and S. laticeps Wu and Lin in Wu, Lin, Chen, Chen and He, 1977, are junior subjective synonyms of B. devdevi and B. lippa, respectively. Sinilabeo yunnanensis Wu, Lin, Chen, Chen and He, 1977, is an available name, and a lectotype is designated for the species. Bangana zhui ( Zheng and Chen, 1983) is a valid species distinct from B. yunnanensis.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network applied to diagnosis of lung cancer

Artificial neural network(ANN) approach was applied to classification of normal persons and lung cancer patients based on the metal content of hair and serum samples obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES) for the two groups. This method was verified with independent prediction samples and can be used as an aiding means of the diagnosis of lung cancer. The case of predictive classification with one element missing in the prediction samples was studied in details, The significance of elements in hair and serum samples for classification prediction was also investigated.

改进的符号时间序列分析方法及其在电机故障诊断中的应用

提出了一种改进的基于符号时间序列分析的电机异常探测方法,该方法自适应地将符号序列中出现符号最多的符号区间重新划分为2个新的符号区间,使得数据密集区间可以分配到相对更多的符号,而数据稀疏区间则分配到较少的符号,提高了符号对于信号变化的灵敏度。电机转子断条故障的诊断实验结果表明:该方法较平均划分区间的方法对于电机异常诊断有着更高的灵敏度以及更好的鲁棒性和可靠性。

基于数字化生产模型的在线故障诊断技术研究

提出了一种基于数字化的生产模型,使用控制图、故障树分析和专家知识,能够进行制造过程实时监控的诊断,该模型提高了故障诊断系统的可靠性,并提供了可实际操作的可视化建模工具。所开发的在线统计过程控制系统能够根据生产事件的监测,动态响应制造过程变化。该系统运用可视化建模工具,根据专家经验进行故障树建模,通过故障树自动生成专家系统诊断规则库,实现诊断知识的自动获取。将该系统应用于汽车变速箱装配过程的检测与故障诊断,验证了方法的有效性。

基于粗糙集-BP神经网络的发动机故障诊断

由于发动机光谱分析监控数据中磨损微粒种类过多,如果将这些微粒信息直接作为神经网络的输入,则存在输入层神经元过多、网络结构复杂等诸多问题。本文将粗糙集引入到发动机故障诊断中来,利用粗糙集在属性约简方面的优势,删除冗余磨损微粒,提取出重要磨损微粒,并将其作为BP神经网络的输入,建立发动机故障诊断模型。该方法降低输入层的神经元个数,简化了网络结构,缩短网络训练时间,并且由于剔除了冗余磨损微粒,减少了由该部分微粒信息不准确而带来的误差,有效提高了故障诊断的精确度。最后通过算例分析验证了相关算法和诊断模型的准确性和有效性。