一种基于Bark域能量分布的噪声分类方法

对不同种类噪声的分类处理,可以提高噪声环境下语音信号处理的性能。为了能够准确地区分各类噪声,提出了一种基于Bark域噪声能量分布特性的噪声分类方法。通过将噪声能量从均匀时频空间映射到Bark空间,构造了一个能够有效区分各种噪声的22维特征向量,并使用支持向量机（SVM）进行模型训练和噪声分类。实验结果表明：所提出的噪声分类方法具有非常高的分类准确率,对用于实验的两种噪声数据集的平均分类准确率分别为99.50%和93.44%。     

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.     

本体支持的生物医学领域元数据异质性与可兼容性研究

利用本体支持数据元素的表示,是提升元数据机器可理解性的重要手段。采用生物医学通用数据元素数据库caDSR中的数据,评价相关的数据元素之间的语义异质性,并利用机器学习对元数据可兼容性进行判别。首先,从caDSR 中选取60对通用数据元素,涉及人口学、生活方式、既往病史和实验室测量等方面。依据ISO/IEC 111179标准抽提数据元素的必要组分,利用NCIT的本体支持,就每对关联数据元素的相似度进行评价。依据数据元素内部各组分的语义相似度,利用支持向量机,对数据元素间的可兼容性做出预测,其准确度超过80%。研究结果显示,目前在caDSR数据库中,对于元数据的定义存在较大的异质性,这些异质性在数据元素的概念域尤其集中。虽然如此,通过机器学习的方法,还是能够依据现有的数据元素的定义实现数据可兼容性的自动判断。研究所建立的方法,对于优化数据元素构建流程、丰富数据标准化工具具有一定的应用价值。     

面向神经内科的信息辅助分析管理系统设计

随着计算机技术在医学领域的发展，医生科研工作者也越来越多应用计算机技术结合ll每床知识与数据进行诊断与科研。结合当前流行的sSH框架，设计并实现了基于wEB的面向神经内科的科研信息辅助管理与分析系统，为神经内科医生提供了一个数据管理与科研平台。     

Screening drug target proteins based on sequence information

Identifying new drug target (DT) proteins is important in pharmaceutical and biomedical research. General machine learning method (GMLM) classifiers perform fairly well at prediction if the training dataset is well prepared. However, a common problem in preparing the training dataset is the lack of a negative dataset. To address this problem, we proposed two methods that can help GMLM better select the negative training dataset from the test dataset. The prediction accuracy was improved with the training dataset from the proposed strategies. The classifier identified 1797 and 227 potential DT proteins, some of which were mentioned in previous research, which added correlative weight to the new method. Practically, these two sets of potential DT proteins or their homologues are worth considering.     

Gene Signature Associated with Upregulation of the Wnt/β-Catenin Signaling Pathway Predicts Tumor Response to Transarterial Embolization

Purpose

To identify gene mutations in tumors undergoing transarterial embolization and explore the relationship between gene mutations and tumor response to embolization.

支持向量机预测食管鳞癌患者术后生存期

目的 应用支持向量机（support vector machine, SVM）建立食管鳞状细胞癌（esophageal squamous cell carcinoma, ESCC）术后生存期预测模型并评估该模型判断ESCC生存期的效能。方法 随访168例接受根治性手术治疗的ESCC患者,分析ESCC临床病理特征和14-3-3σ、热休克蛋白gp96、巨噬细胞移动抑制因子（migrationinhibitory factor, MIF）等3个蛋白的表达规律与ESCC生存期的相关性;应用Matlab软件进行SVM运算,对训练组128例ESCC患者建立最优预后分类模型ESCC-SVM,并用测试组40例患者验证分类效率,ROC曲线分析ESCC-SVM及其他预后相关因子对高低死亡风险ESCC的识别能力。结果 ESCC-SVM由性别、T分期、组织学分级、淋巴结转移、TNM分期、14-3-3σ和gp96等7个最优属性组成,该模型区分训练组和测试组ESCC五年整体生存率的最大AUC分别为0.96、0.86、准确率分别为97.7%、90.0%,明显优于目前临床应用的TNM分期（准确率分别为62.5%、67.5%）及其他各临床病理属性。Cox多因素比例风险回归模型分析发现年龄、T分期、gp96和ESCC-SVM是影响ESCC术后生存期的独立因素。ESCC-SVM与性别、T分期、组织学分级、淋巴结转移、TNM分期和14-3-3σ均显著相关。结论 本研究建立的ESCC-SVM为预后评估、临床治疗方案选择及个体化治疗提供了理论依据。     

疲劳驾驶识别中的脑电信号特征选择算法和支持向量机模型研究

针对疲劳驾驶识别中脑电特征选择和分类模型,提出采用粗糙集理论的离散化算法对通道和脑电信号特征量进行选择,选用支持向量机作为疲劳驾驶识别模型,并将疲劳误判风险作为支持向量机模型参数进行模型优化。针对5名受试者的实验结果表明,与主分量方法相比,粗糙集离散化算法选取的特征量较少,以0.8为相容度阈值,在208个候选特征中选择的特征数为2～4个,不同被试者选取的特征不同且对建立支持向量机识别模型有影响;疲劳误判风险控制参数可以达到调节支持向量机识别模型误判风险。     

针对个体差异的心理压力评估

慢性心理压力会带来一系列的病理、生理风险,直接影响健康。有效地评估心理压力,一直是心理压力研究中的热点问题。在心理压力评估过程中,个体差异是影响评估效果的关键。本研究针对评估心理压力/非压力反应中个体差异问题,以表面肌电信号作为评估参数,以高校即将毕业的学生人群为对象,提出了一种改进的支持向量机心理压力评估算法。算法通过对样本聚类,并将聚类信息赋予支持向量机的损失函数,实现训练样本的筛选,针对筛选后出现两类样本不平衡问题,为损失函数赋予权重来降低分类器的预测倾向性,减少训练模型的误差,补偿不平衡样本数据所造成的影响。心理压力评估分类正确率由改进前的70.34%,提高到79.31%,算法运行时间由改进前的2026.5 s减少到541.3 s。结果表明,该算法可以有效地解决个体差异对于心理压力评估效果的影响,同时降低了分类器的计算复杂度,为心理压力评估中个体差异研究提供一种可行的方案。     

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine

Purpose:

To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images.

Materials and Methods:

Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1‐weighted, T2‐weighted, and proton density/fluid‐attenuated inversion recovery images.

Results:

The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra‐ and inter‐operator coefficient of variations.

Conclusion:

The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI. J. Magn. Reson. Imaging 2010;32:24–34. © 2010 Wiley‐Liss, Inc.