厦漳泉大都市区建设及对策研究

本文在研究大都市区形态的基础上,分析了核心城市的城市化及厦漳泉大都市区发展现状,提出了培育厦漳泉大都市区的战略构想,通过提升核心城市城市化质量,打造两岸交流合作前沿平台,构筑城市联盟推进同城化,提升综合承载和辐射带动能力等,壮大大都市区的经济规模和竞争力

基于替换概率的闪存数据库缓冲区替换算法

闪存具有和传统磁盘不同的特性，包括写前擦除、异地更新、读写延迟非对称等.传统的面向磁盘的缓冲区替换算法无法在闪存数据库系统中获得较好的性能.文中提出了一种新的面向闪存数据库的缓冲区替换算法——APB-LRU，其特点：(1)该算法将缓冲区分为冷区和热区，用来捕获数据访问频度，前者用于存放只访问过一次的数据页，后者用于存放至少访问过两次的数据页；(2)采用了其它研究所没有的概率替换机制，即以较大的概率替换冷区中的干净页，以较小的概率替换冷区中的脏页，从而避免了冷脏页长期驻留缓冲区的情况，提高了命中率，获得了较好的整体性能；(3)设计了冷、热区比例动态变化机制，可以根据工作负载的变化动态调整冷、热区所占缓冲区的比例，从而使得替换算法在不同的负载模式下都可以取得较好的性能.基于不同测试数据集的大量实验结果表明，APB-LRU算法具有比其它已有的算法更好的性能.厦门大学基础创新科研基金(中央高校基本科研业务费专项资金)(2011121049,2012121030);国家自然科学基金(61001013,61102136,61202012);福建省自然科学基金(2011J05156,2011J05158,2013J05099)资

Under-sampling Method Research in Class-Imbalanced Data

提出了一种处理不平衡数据的降采样方法,以解决机器学习中分类器在训练时因为样本集中的样本类别不平衡而导致过拟合造成分类器性能下降的问题.利用k-MEAnS方法,对大类样本进行聚类并提取聚类中心,获得与较小样本集样本数目近似的样本,组成新的样本集用以训练.为了避免在小类样本数目较小的情况下,单纯使用聚类降采样算法造成训练集样本的过度稀疏,使用SMOTE过采样算法结合聚类降采样,既避免了SMOTE为样本集引入较多的噪声,又有效地解决了训练集样本稀疏的问题.6组uCI测试数据和5组生物信息学实验证明了它在对类别不平衡数据进行降采样上的有效性.An under-sampling method is proposed for class-imbalanced data classification,which resolves the classifiers' over fitting phenomenon to enhance classification ability.Using K-Means method to clustering big class set and extract cluster center then merge with small class sample to generate a balanced sample set for classifiers' training.To avoid the minimum class is too small then purely using clustering under-sampling would lead the training set's sample be excessively sparse,using SMOTE over-sampling algorithm combine cluster under-sampling algorithm not only avoid induce too much noise but also resolve the problem of shortage of sample.Six groups testing data and five groups' biological information experiment certificate it validity on class-imbalanced data set.国家自然科学基金项目(61001013;61102136);国家“九七三”重点基础研究发展计划基金项目(2007CB311005);国防基础科研计划项目(B1420110155

一种高温燃料电池用阴极催化层及其制备和膜电极

一种高温燃料电池用膜电极，阴极催化层中除含有氧还原催化剂、粘结剂和疏水剂外，还含有磷酸和硅油。磷酸作为液体电解质在高温条件下有助于质子的传导，但因其溶氧性不好，将其添加到阴极催化层中不利于氧气的传输；硅油是一种由聚硅氧烷组成的液体制品，它具有储氧能力强、氧气在其中的传输速度快两个优点，因此，在制备膜电极过程中将其随磷酸均匀的添加到阴极催化层中可以避免因氧传质差导致的电池性能降低

Application of ArtiFicial Neural Networks For Hydrocarbon Gas Mixture Analysis

An array composed of sixtorganiceen metal oxide semiconductor gas sensors was constructed to analyze gas mixtures quantitatively.The responses of the sensor array to ethane, propane and propylene were treated by three-layer artiFicial neural networks (ANN)with the method of error back-propagation and partial least-squares (PLS)- The pattern recognition results indicated that the concentration predicted with ANN is better than that with PLS.The average prediction errors For ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively, in the ANN prediction.国家自然科学基金;福建省自然科学资

Development of particle swarm optimization algorithm

Conference Name:6th International Conference on Computer Science and Education, ICCSE 2011. Conference Address: Singapore, Singapore. Time:August 3, 2011 - August 5, 2011.Particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in the future investigations are discussed. ? 2011 IEEE

Identify content quality in online social networks

The flooding of low-quality user generated contents (UGC) in online social network (OSN) has been a threat to web knowledge management systems. Recently several domain-specific systems have been developed addressing this problem, for example, predict correct answer in QA community; recognise reliable comment in products review forums etc. Major drawback of most research efforts is the lack of a general framework applicable to all OSNs. In this study, the authors start by analysing the effects of distinguishing features on UGC quality in different types of OSNs. Extensive statistical analysis leads to the discovery of existence of diverse patterns of human information sharing activity in dissimilar OSNs. This discovery is employed as prior knowledge in the classification framework, which decompose the original highly imbalanced problem into several balanced sub-problems. Ensemble classifiers are adopted in samples from clusters generated by incompact features. Experiments show the proposed framework is both effective and efficient for several OSNs. Contributions of this study are two-fold: (i) model posting activity in different types of OSNs; (ii) propose novel classification framework to identify UGC quality

一种多孔介质液汽迁移评价装置及其应用方法

一种多孔介质液汽迁移评价装置，包括第一加热板、液腔、样品架、汽腔和第二加热板；液腔和汽腔分别为二个密闭的腔体，于液腔的下壁面和汽腔的上壁面上分别设有通孔，作为液腔开口和汽腔开口；样品架为二个环形支架，二个环形支架分别相对置于待测多孔介质的上下两侧，环形支架与待测多孔介质紧密贴接；液腔和汽腔分别置于二个环形支架远离待测多孔介质的一侧；且液腔开口和汽腔开口分别对应于环形支架的中空部位；于液腔外壁面上设有第一加热板，所述第一加热板用于为液腔加热；于汽腔外壁面上设有第二加热板，所述第二加热板用于为汽腔加热。与现有技术相比，本发明装置结构简单，易于加工制造；测试过程简单、准确，可有效提供数据；测试范围广，可测试不同温度条件下不同液体在不同多孔介质中的液体迁移量，应用领域广泛，不受限制

Application of artificial neural networks for hydrocarbon gas mixture analysis

An array composed of sixtorganiceen metal oxide semiconductor gas sensors was constructed to analyze gas mixtures quantitatively. The responses of the sensor array to ethane, propane and propylene were treated by three-layer artificial neural networks (ANN) with the method of error back-propagation and partial least-squares (PLS), The pattern recognition results indicated that the concentration predicted with ANN is better than that with. PLS, The average predict-ion errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively, in the ANN prediction