森林植被改善对鄱阳湖流域径流和输沙过程的影响

以河流基流为切入点,研究流域植被调节径流、水土保持等微观作用影响大中型河流湖泊径流过程和水沙过程宏观效应的机理。鄱阳湖流域60年来天然降水没有发生趋势性变化。流域森林覆盖率由34.73%上升到63.00%,植被质量改善,赣江等入湖河流基流增加83 m~3/s,河流输沙量减少。2000年以后和2000年以前相比较,枯水期降水径流系数增大,年流量过程平坦化,一定程度上减小洪灾风险,有利于水资源利用和生态环境保护。2001年以后进入鄱阳湖泥沙平均每年减少1 007×10~4t;出湖泥沙增加314×10~4t。因此,鄱阳湖入江水道由淤积转变为冲刷,但出湖流量过程没有趋势性变化。     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

林区火灾监测报警系统设计及实现

设计了一种林区火灾监测及报警系统。林区自组织网络采用ZigBee技术和无线射频芯片CC2530设计。其数据采集节点上配置的温湿度传感器和红外火焰传感器可以实现对林区环境的实时监测,并辅以GPRS网络技术将数据远程传输至监测中心,利用数学建模及软件分析对突发情况下火场的范围、火势的大小及蔓延形势进行运算,并通过图形化的人机交互界面实现对林区的火灾监测。为延长野外环境下系统工作时间,对终端节点采用休眠技术,对路由节点和协调器节点采用太阳能供电技术。实验表明,该系统能稳定有效工作,达到准确直观地对火灾情况进行判断和报警的要求。     

基于机器学习的肺部CT图像非刚性配准误差预测方法

配准误差评估通常由人工完成,耗时费力;常用的Dice测度只关注组织边缘的配准误差,难以评估组织内部配准结果。针对以上问题,提出一种基于机器学习的肺部CT图像非刚性配准误差预测方法(PREML)。该方法首先构建形变场统计特征、形变场物理保真度特征和图像相似性特征三类特征,然后通过池化方法扩充特征数量,最后使用随机森林回归方法预测非刚性配准误差,并且使用自适应随机扰动方法模拟肺部配准误差空间分布,进一步提升形变场统计特征的配准误差表征能力。在三个肺部CT图像数据集上进行训练与测试,其配准误差预测结果与金标准之间的平均绝对差异为1.245±2.500 mm,预测性能优于基线方法。结果表明,PREML方法具有预测精度高、鲁棒性强的特点,可提升配准算法在临床应用的有效性和安全性。     

基于分类外形搜索的人脸特征点定位

针对传统由粗糙到精准的人脸外形搜索方法,其每一次外形搜索需要在整个外形搜索空间进行,提出一种基于分类的外形搜索方法。该方法始于一个包含不同人脸形状的外形搜索空间,首先利用基于相关性的特征选择方法对随机森林分类器进行优化,利用训练的随机森林分类器将外形搜索空间分为若干个外形搜索子空间;然后根据输入样本和随机森林分类器确定与当前外形最接近的外形搜索子空间,并计算对应子空间的中心和对应样本的后验概率分布,方便后续阶段更好地进行外形搜索;最后采用级联回归进行人脸特征点定位。在300-W数据集上的实验结果表明,此方法不仅有效降低了外形搜索的时间,同时在无约束环境中具有良好的鲁棒性。     

Relations between damage symptoms and nutritional status of Norway spruce stands (Picea abies Karst.) in southwestern Germany

Since the mid-1970s new types of forest damage are observed in the Federal Republic of Germany. Typical damage symptoms in coniferous tree species are needle yellowing, reddening and premature loss of older foliage. Needle analysis carried out in damaged Norway spruce stands indicated that these phenomena, most of all needle yellowing, are influenced by the nutritional status of the trees. For the investigated sites Mg but also other elements, particularly P, S, K, Ca and Zn may play a causal role. As N is the most abundant element in the plant its form of uptake is important for a balanced nutrition. On the contrary to NO₃-N high NH₄-N uptake reduces the uptake of cations, particularly Mg and Ca, eventually inducing foliar discoloration symptoms on specific sites. Fertilizer experiments indicated that Mg fertilization is an appropriate tool to mitigate forest damages associated with Mg deficiency for a sustained time period. The application of N in the form of NH₄-N may impede Mg uptake. This mechanism may cause severe damage in forest areas receiving continuously high NH₄-N deposition.     

林产化学加工学科的现况与发展前景

本文了介绍了并论述了林产化学加工学科所含的五个专业方向，木材化学加工工程，树木提取物化学工程，林产生物化工，木材保护与改性，林产化工装备与控制的研究内容，工业现状，存在问题与发展前景。     

基于SURF与Hough森林的人脸检测研究

为实现复杂场景中的人脸检测与定位,提出了一种基于快速鲁棒特征SURF与Hough森林的人脸检测算法。采用SURF局部特征构建Hough森林分类器,每个叶子节点存储类别信息与特征点到达目标中心的偏移量,在图像局部外观与Hough投票之间建立映射关系,生成有监督的判别式的码本,获得可靠的概率Hough投票,以此预测目标中心位置,提高了检测精度。与此同时,采用SURF局部特征提取图像兴趣点有助于减小计算量、加快检测速度。实验证明了所提算法的有效性。     

基于决策树方法的云南省森林分类研究

森林分类对于理解森林生态系统结构和功能具有重要意义。由于云南省地形和森林类型复杂,首先按云南省的16个行政区划将全省Landsat TM影像分为对应的16个区域。以TM波段1~5和7,以及由植被指数、缨帽变换、主成分变换、DEM组成的18个变量组,统计训练样本光谱值均值变化和光谱值与频率间的关系。利用交点计算公式计算类间最佳分类界点进而建立决策树,逐一分离各区的所有森林类型,将分类结果合并得到云南省阔叶林、针叶林和针阔混交林类型分布图。最后将分类结果与监督分类中的最大似然比法的分类结果进行对比。结果表明:监督分类的总体分类精度为74.39%,Kappa系数为0.63,决策树方法的总体分类精度为86.61%,Kappa系数为0.80,说明决策树方法可以提取高精度的云南省森林类型,进而为该区域森林叶面积指数和生物量反演等研究提供基础数据支持。