锥形配流副弹流润滑模型与数值计算方法

为求解高压条件下锥形配流副的润滑模型,该文提出了综合应用有限差分法、液阻网络法和约束最优化复合形法的二元二目标复合形法,研究表明了模型解的存在性和唯一性,并通过实例应用二元二目标复合形法对润滑模型进行了求解。实例结果显示,平衡油槽的无量纲压力随工作压力的变化保持恒定,配流间隙随工作压力增大呈线性减小,而偏心率在工作压力范围内均略小于0,最小为-0.036。研究为锥形配流副弹流润滑特性的分析及其设计提供了参考。     

基于北斗船位数据的流刺网网次和方向提取方法研究

渔船监测系统(vessel monitoring system,VMS)在渔业管理中发挥着重要作用,一直以来都受到各国及渔业组织的重视,我国近海渔船主要采用基于北斗的VMS系统。刺网是我国主要的捕捞作业方式之一,提出一种使用北斗数据提取流刺网作业网次以及网长和方向的方法。该方法使用阈值综合判别的方法判断捕捞作业的状态,通过航速、空间距离、时间间隔和航向变化的阈值判别作业时收网状态的船位点,然后根据收网状态的起始点判定放网状态的起始点。根据范例中提取的516组网次,使用程序判别的船位点和人工判断的船位点有74%相同,表明有较好的一致性,具有精度高、处理快速、实时程度高的特点。该方法可为我国渔业管理和相关研究提供新的手段,并可应用于捕捞控制、海上执法等多个领域。     

Who benefits from international tourism in small Pacific islands? Value capture in the hotel industry in Hawaii

There is a consensus among small Pacific islands that the extent to which they benefit from international tourism largely depends on how much of the value created by tourism remains in the local economy. This study examines how the value created in the hotel industry is distributed among the key stakeholders in a small Pacific island context. We used aggregated income statements of full-service and limited-service hotels from the STR Inc., a hotel industry data company, to calculate the value distribution among the key stakeholders in the hotel industry of a small Pacific island. We found that labour and owners captured most of the value created, whereas hotel management companies and franchisors captured a small share of the value. Our results suggest that tourism workers' ability to take united action and hence to negotiate higher wages will result in higher value capture by local labour and less value leaking out of the local economy. Our results also reveal that foreign ownership in the hotel industry is the single largest cause of economic leakages. The study has several implications for the tourism-based growth policies in small Pacific islands.     

基于ICS优化RBF的水库水质三维预测方法

针对已有水质预测模型在数据降噪、网络参数初始值设置和优化、精度提高等方面能力的不足,构建了一种优化的水质三维预测模型。利用主成分分析算法筛选出水质关键参数,并基于自适应噪声的完全集合经验模态分解算法结合小波阈值模型对三维水质参数和气象数据降噪处理,使用3维卷积神经网络(Three-dimensional convolutional neural networks, 3-D CNN)提取出特征数据集,自编码器(Autoencoder, AE)获得径向基函数(Radial basis function, RBF)网络参数初始化值,改进布谷鸟搜索算法(Improved cuckoo search, ICS)优化更新网络中超参数动态初始化值。广东省湛江市徐闻县大水桥水库区域22个典型在线监测站点以及6个手持监测点的实测数据对比验证结果表明,浊度和藻密度分别与总氮含量强正相关,叶绿素含量与气温强正相关,所提出的水质预测模型在5个典型精准性评价指标方面优于已有文献方法。研究成果可为管理部门和研究者对水质监测提供参考。     

基于双分支卷积网络的玉米叶片叶绿素含量高光谱和多光谱协同反演

针对智慧农业中叶绿素的精准预测问题,本文提出了基于双分支网络的玉米叶片叶绿素含量高光谱与多光谱协同反演的方法。使用欠完备自编码器进行数据降维,捕捉数据中最为显著的特征,使降维后的数据可以代替原始数据进行训练,从而加快训练效率,使用双分支卷积网络将多光谱数据用于填充高光谱数据信息,充分利用高光谱数据的空间细节信息,再结合1DCNN建立玉米叶片叶绿素含量预测模型。结果表明,与传统降维算法相比较,欠完备自编码器处理后预测结果最佳,决定系数R2为0.988,均方根误差（RMSE）为0.273,表明使用欠完备自编码器进行降维可以有效提高数据反演精度;与单一的高光谱数据反演模型和多光谱数据反演模型相比,双分支卷积网络预测模型均取得较优的预测结果,R2在0.932以上,RMSE均在1.765以下,表明基于双分支卷积网络的高光谱与多光谱图像协同反演模型可以有效地利用数据的特征;对于其他数据结合本文提及的双分支卷积网络模型进行反演,其R2均在0.905以上,RMSE均在2.149以下,表明该预测模型具有一定的普适性。     

基于遥感多参数和IPSO-WNN的冬小麦单产估测

冬小麦是我国的主要粮食作物之一。为进一步准确地估测冬小麦产量,以陕西省关中平原为研究区域,选取冬小麦主要生育期与水分胁迫和光合作用等密切相关的条件植被温度指数（VTCI）、叶面积指数（LAI）和光合有效辐射吸收比率（FPAR）作为遥感特征参数,采用改进的粒子群算法优化小波神经网络（IPSO-WNN）以改善梯度下降方法易陷入局部最优的缺陷,并构建冬小麦产量估测模型。结果表明,IPSO-WNN模型的决定系数R2为0.66,平均绝对百分比误差（MAPE）为7.59%,相比于BPNN（R2=0.46,MAPE为11.80%）与WNN（R2=0.52,MAPE为9.80%）,IPSO-WNN能够进一步提高模型的精度、增强模型的鲁棒性。采用灵敏度分析的方法探究对冬小麦产量影响较大的输入参数,结果发现,抽穗-灌浆期的FPAR对冬小麦产量影响最大,其次拔节期的VTCI、抽穗-灌浆期和乳熟期的LAI以及返青期和拔节期的FPAR对冬小麦产量的影响较大。通过IPSO-WNN输出获取冬小麦综合监测指数I,构建I与统计单产之间的估产模型以估测关中平原冬小麦单产,结果显示,估测单产与统计单产之间的R2为0.63,均方根误差（RMSE）为505.50kg/hm2,相比于前人的研究较好地解决了估产模型存在的“低产高估”的问题,因此,本文基于IPSO-WNN构建的估产模型能够较准确地估测关中平原冬小麦产量。     

我国加入WTO后外文期刊资源建设的新思考

影印刊因我国加入W TO后履行对知识产权保护承诺而出现断档,原版刊又因价格昂贵使各馆望而生畏。如何克服经费困难,订购原版刊,并对其加大开发利用力度,作者对此提出了应取的对策。     

NAS、SAN的融合与数字图书馆

随着计算机技术的发展和广泛应用,数据量不断增加从而对网络存储提出了更高的要求,并由此诞生了许多新的网络存储技术,其中较为突出的是NAS和SAN,而NAS和SAN由于各自的优缺点及互补性出现了相互融合的趋势。NAS与SAN融合产生的巨大优势适应并推动了数字图书馆发展。     

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.