Shift-share Analysis on International Tourism Competitiveness——A Case of Jiangsu Province

Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds of extended shift-share models have been advanced and put into practice in economic studies, but few have hitherto been introduced and applied to the tourism research in China. Moreover understanding the spatially competitive relationship is of paramount importance for marketers, developers, and planners involved in tourism strategy development. Based on international tourism receipts from 1995 to 2004, this study aims at probing into the spatial competitiveness of interna- tional tourism in Jiangsu Province in comparison with its neighbors by applying a spatially extended shift-share model and a modified dynamic shift-share model. The empirical results illustrate that exceptional years may exist in the ap- plication of dynamic shift-share models. To solve this issue, modifications to dynamic shift-share model are put forward. The analytical results are not only presented but also explained by the comparison of background conditions of tourism development between Jiangsu and its key competitors. The conclusions can be drawn that the growth of international tourism receipts in Jiangsu mainly attributes to the national component and the competitive component and Zhejiang is the most important rival to Jiangsu during the period of 1995-2004. In order to upgrade the tourism competitiveness, it is indispensable for Jiangsu to take proper positioning, promoting and marketing strategies and to cooperate and integrate with its main rivals.

Industrial Structure and Competitive Strength of 31 Provincial Districts——A GIS-based Dynamic Shift-Share Analysis

By using the dynamic shift-share analysis, the industrial structure and competitive strength of 31 provincial districts except Taiwan, Hong Kong and Macao are studied by taking the GDP of the three industries as the research entrance and the whole nation as the reference district. The industrial structure and competitive strength of each provincial district is measured. Through the analysis of pertinence, the correlation degree of industrial structure and industrial competitive strength to economic growth is analyzed. The results show that the industrial competitive strength is closely related to the economic growth of the 31 provincial districts, but the contribution made by the industrial structure to economic growth is insufficient and the effect of industrial structure does not match with that of industrial competitive strength. According to industrial competitiveness and industrial structure effect, 31 provincial districts of the whole nation are divided into 4 types and the relevant countermeasures of the four types are put forward.

Industrial Structure Evolution and Economic Growth in Dingxi City Based on Shift-Share Method and Location Quotient Analysis

According to the statistical data in the years 2004-2008, both Shift-Share Analysis and Location Quotient Analysis are used to compare the economic development status of counties (districts) in Dingxi City in the years 2004 and 2008. Advantages and disadvantages of industrial structure and competitiveness are analyzed, as well as the impacts of existing industrial structure on economic growth. Development direction and development focus of primary, secondary and tertiary industries are found out. Countermeasures for accelerating the economic development of counties (districts) in Dingxi City are put forward, such as increasing the inputs in characteristic agriculture, promoting the development of primary industry, speeding up the construction of large and medium industrial enterprises, enhancing the development of secondary industry, strengthening comprehensive environmental improvement in tourist area, and actively developing tertiary industry.

The Fishery Industrial Structure in China Based on the Application of Shift-Share Analysis

Taking sixteen main fishery production regions as the study objects,this paper is to examine the fishery industrial structure and investigate the correlation between the economic growth of the fishery industry and the fishery industrial structure during the period 1997-2013 by the shift-share analysis. The study results show that( i) four types of regional industrial structure exist in the fishery industry in China;( ii)all the three sectors-fishing production,fish processing( manufacturing) and fish marketing( distribution),have emerged with large potential for promoting the national fishery industry growth,especially for the latter two;( iii) the factors of industry-mix and competitive position have significantly different impacts on the growth of fishery industry in different main fishery production regions. This study is one of the comprehensive studies on the status of regional fishery industrial structure,and the competitiveness of regional fishery industry in China.

A comparative study of manufacturing industrial competitiveness in eastern coastal provinces and cities of China based on shift-share analysis model

Manufacturing industry is an important symbol which weighs a local economy comprehensive strength and modernized level, and a mighty manufacturing industry is a reliable guarantee which realizes the industry modernization. The manufacturing industry in eastern coastal provinces and cities of China, including Shandong, Jiangsu, Zhejiang, Shanghai and Guangdong, holds a quite high proportion, and is competitive. But as to the interior, the manufacturing industry competitiveness of five provinces and cities actually has obviously gradient difference. The concrete profession＆amp;#39;s competitiveness also has the characteristic respectively. This paper analyzes the competitiveness of manufacturing industry in five provinces and cities by the shift-share analysis, obtains a conclusion that Shandong and Zhejiang have the competitive advantage in the traditional manufacturing industry, that Jiangsu＆amp;#39;s and Guangdong＆amp;#39;s competitiveness lies in capital, technology-intensive industry, but that Shanghai manufacturing industry＆amp;#39;s competitive advantage is not obvious, and then proposes strategy to promote the manufacturing industry in the eastern coastal provinces and cities.

基于SQL Server Analysis Service(SSAS)的土地地类关系空间数据挖掘

基于SQL Server Analysis Services(简称SSAS)提供的Microsoft关联规则挖掘算法和事务数据挖掘功能,通过利用Arc GIS软件、空间数据库引擎Arc SDE和数据库SQL Server软件,提出了一种新的土地地类关系挖掘实现方案。首先结合空间数据挖掘(Spatial Data Mining,SDM)相关技术方法,以土地利用数据库为基础,实现空间数据提取;然后通过空间关联操作将空间信息转化为事务,最后在SSAS中创建多维数据集,完成相关数据挖掘任务。基于某市实例土地利用数据库,采用该方法探测相邻地类间的隐含关系,通过建立相邻地类图斑空间关联规则挖掘模型,设置不同的参数,得到了一系列比较实用合理的关联规则,通过实践证明了这种方案的有效性。

基于SSA-RBF神经网络的煤自然发火预测模型

为解决传统煤自燃预测模型预测状态单一和预测精度不高的问题,提出基于麻雀搜索算法(SSA)优化的径向基(RBF)神经网络煤自然发火预测模型。首先,采用程序升温试验分析煤样指标气随温度的变化特征,将煤自然发火过程按煤温分为缓慢(80≤t_(i)<120℃)、加速(120≤t_(i)<160℃)和激烈(t_(i)≥160℃)3个氧化阶段,同时分析这3个阶段指标气与煤温的灰色关联度;其次通过不同维度测试函数检验粒子群算法(PSO)、灰狼算法(GWO)和SSA算法性能;最后利用6个矿区数据验证基于SSA-RBF神经网络的煤自燃预测模型的优越性。结果显示,缓慢氧化阶段CO/ΔO_(2)、CO、C_(2)H_(4)这3种指标气体与煤温的灰色关联系数最大;而加速氧化阶段C_(2)H_(4)/C_(2)H_(6)、CO/ΔO_(2)、CO_(2)/CO_(3)种指标与煤温的灰色关联系数最大。3种不同维度函数的测试结果表明:SSA与PSO、GWO相比具有更好的全局搜索能力和稳定性,其收敛速度更快;神经元数量为5个、迭代次数为300次时,SSA-RBF神经网络预测模型对缓慢氧化和加速氧化阶段的预测准确性分别达到了99%和93%。

SIMULATION OF CRACK DIAGNOSIS OF ROTOR BASED ON MULTI-SCALE SINGUUR-SPECTRUM ANALYSIS

In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.

基于ISSA-SVM的钻井卡钻事故预测

为预防钻井过程中卡钻事故的发生,通过提出了一种改进麻雀搜索算法(improved sparrow search algorithm,ISSA)优化支持向量机(support vector machines,SVM)的预测模型方法(ISSA-SVM),在发现者位置更新公式中引入一种改进的自适应非线性惯性递减权重;在警戒者位置更新公式中引入莱维飞行策略。利用主成分分析法(principal component analysis,PCA)对外国某大型油田的实测钻井数据进行降维处理,并利用惩罚参数和核参数进行卡钻事故的预测。实验结果表明:ISSA-SVM的预测准确率高达85.1852%,且收敛速度更快,可见ISSA-SVM可有效预测钻井卡钻事故。

Wavelet De-noising of Speech Using Singular Spectrum Analysis for Decomposition Level Selection

The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise.The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech.The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.

基于FA-ISSA-PPR模型的旋风分离器分离效率预测

旋风分离器是气田开发中常用的气固分离设备,准确预测旋风分离器的分离效率对于指导其结构设计和方法优化具有重要意义。在对数据集进行相关性分析的基础上,采用因子分析(factor analysis, FA)简化变量,降低预测模型的复杂程度,利用改进的樽海鞘群算法(improved salp swarm algorithm, ISSA)对投影寻踪(projection pursuit regression, PPR)的模型参数进行优化,形成FA-ISSA-PPR组合模型。结果表明,利用FA模型,原数据集的10个变量可以简化合并为4个公因子,分别代表尺寸参数、颗粒沉降特性、粒子运行轨迹和等效分割粒径对分离效率的影响;与半经验模型和其余机器学习模型相比,组合模型在预测精度和训练时间上具有一定的优越性,在测试样本上的平均绝对误差(MAE)为0.005 91,R^(2)可达0.995,证明了其在小样本、非线性数据分析上的准确性、鲁棒性和泛化性。

基于遗传算法优化VMD-ESSA的HIFU回波信号降噪研究

由于高强度聚焦超声(HIFU)信号夹杂环境噪声,提出利用遗传算法优化变分模态分解(VMD)并结合能量均值法(EAM)及奇异谱分析(SSA)来对其进行降噪。遗传算法使得VMD具有更强的抗干扰性,奇异谱分析能够弥补能量均值去噪不完善。仿真实验表明,所提方法与GA-VMD-E、GA-VMD-SSA、VMD-ESSA降噪方法相比,重构后的信号的信噪比更高,均方根误差和最大误差更小。在实测信号中从频谱和降噪率两个角度证明了该方法的优越性,为通过HIFU回波信号分析生物组织损伤提供了一种更有效的预处理方法。

基于SSA-SVM的道路事故预测模型

将道路事故特征因素和环境因素作为自变量,事故严重程度作为因变量,采用SVM构建道路事故预测模型,并使用SSA优化模型中的最优参数组合,最终建立基于SSA优化的SVM道路事故预测模型,模型预测准确率优于广义线性预测模型和BP神经网络预测模型。经过模型致因分析,得到三项对道路事故严重程度具有显著影响的自变量,分别为机非隔离形式、监控设施和最近交叉口交通组织形式,可作为道路交通安全管控的核心控制参数。

基于广义解调和SSA-SVM模型的高速道岔区晃车诊断方法

为解决传统高速铁路道岔区晃车状态诊断过度依赖人工巡检的问题,提出一种基于广义解调和麻雀搜索算法(SSA)优化支持向量机(SVM)模型(SSA-SVM)的道岔区晃车状态诊断方法。首先,基于广义解调对车体横向加速度进行分解,提取不同频率模态分量,结合分量信息以及轨道几何信息,进一步计算道岔区晃车诊断特征指标;然后,采用SSA-SVM模型作为道岔区晃车分类诊断模型,提出基于该模型的道岔区晃车状态诊断方法;最后,以我国某高速铁路道岔区实测数据为例进行案例分析,验证该方法的有效性。结果表明:与基于误差反向传播算法模型(BP)、SVM模型、粒子群优化算法(POS)优化的误差反向传播算法模型(POS-BP)和粒子群优化算法优化的支持向量机模型(POS-SVM)的道岔区晃车状态诊断方法相比,采用SSA-SVM模型的道岔区晃车状态诊断方法不仅收敛速度快、精度高,而且在特征较少的情况下仍能保持94.8%的高诊断精度。

基于SSA-ANFIS模型的BDS-3卫星钟差短期预报

针对卫星钟差时间序列具有非线性和非平稳的特性,以及趋势分量与随机分量相互干扰可能会影响预报精度的问题,提出一种以奇异谱分析(singular spectrum analysis, SSA)为基础,融合自适应模糊神经网络(adaptive neuro-fuzzy inference system, ANFIS)的卫星钟差预报模型SSA-ANFIS。首先利用SSA对钟差一次差序列进行分解和重构,从而得到趋势项和残差项;然后,使用ANFIS对重构分量进行预报,并将预报结果叠加还原,得到最终预报钟差值;最后,通过实验对比SSA-ANFIS与GM、QP、LSTM和ANFIS模型的预报效果。结果表明,相较于LSTM和ANFIS模型,该模型预报精度分别提高25.7%~40.7%和39.4%~45.7%。

基于SSA-Hurst-ARIMA组合模型的船舶柴油发电机组故障特征短期预测

为提高船舶柴油发电机组故障特征短期预测精度,建立基于奇异谱分析(Singular Spectrum Analysis,SSA)、Hurst指数、自回归移动平均(Auto-Regressive Integrated Moving Average,ARIMA)的组合预测模型。以某试验中船舶柴油发电机组运行数据为基础,选取增压器滑油压强数据,对比分析单一ARIMA模型、SSA主成分-ARIMA组合模型和SSA-Hurst-ARIMA组合模型的预测效果。结果表明,SSA-Hurst-ARIMA组合模型的预测效果优于单一ARIMA模型和SSA主成分-ARIMA组合模型,更适合应用于船舶柴油发电机组故障特征的短期预测。

基于SSA-PSO-LSTM模型的电离层TEC预报

受多种因素影响,电离层电子总含量(TEC)时间序列具有非线性、非平稳性特征,为提升长短期记忆(LSTM)神经网络模型在电离层TEC预报中的精度,本文在该神经网络模型的基础上,引入奇异谱分析(SSA)与粒子群优化(PSO)算法,构建了新的SSA-PSO-LSTM模型。一方面,利用了SSA对TEC时间序列进行数据预处理;另一方面,利用粒子群优化算法改进LSTM神经网络模型参数。选用欧洲地球参考框架(EUREF)提供的格网点电离层TEC时间序列数据进行实验,实验结果表明,在磁平静期与磁暴期,该组合模型的TEC预报结果均方根误差分别为0.28个总电子含量单位(TECu)、0.83个TECu,平均相对精度分别为96.35%、91.33%,均优于对比模型,验证了本文提出的组合预报模型的有效性与优越性。平均相对精度分别为96.35%、91.33%,均优于对比模型,验证了本文提出的组合预报模型的有效性与优越性。

基于SSA和AR模型的海面变化预测试验

以吴淞站1955-2001年月平均潮位序列为基础,采用奇异谱分析(SSA)与自回归模型(AR)相结合的方案(SSA+AR),进行了月平均潮位预测试验。基本思路是对SSA分析的结果选择若干有意义的分量进行序列重建,借助于自回归模型进行分量预测,再对它们进行叠加,从而建立预测模型。本文以1955-1996年数据为基础建立模型,1997-2001年数据作为验证,检验结果表明,两种方法的结合使用显示了较好的效果。

自身抗原52 kD-Ro/SSA的序列分析及抗原性预测

目的：分析自身抗原５２ｋＤＲｏ／ＳＳＡ的序列结构，预测其抗原位点。方法：将自身抗原５２ｋＤＲｏ／ＳＳＡ的氨基酸序列输入“蛋白质结构预测”软件包，分析蛋白质分子亲水性、表面可及性、柔性，并模拟其二级结构，预测其主要抗原位点。结果：自身抗原５２ｋＤＲｏ／ＳＳＡ是多抗原位点，其氨基酸序列中１２０～１３０、３００～３２０、３６０～３８０位间抗原性较强。结论：确定５２ｋＤＲｏ／ＳＳＡ的抗原优势表位，为研究抗原抗体间相互作用及其疾病机制打下基础。

基于SSA和MGF的海面变化长期预测及对比

海面变化预测受到建模思路、方法选择、数据长度及数据质量等因素的影响,导致了海面变化预测的不确定性。本文以国内6个验潮站自20世纪50年代以来的月平均潮位序列为基础,采用奇异谱分析(SSA)与均值生成函数(MGF)模型相结合的方案,以各站位最初20余年数据为基础建立预测模型,以后续年份的实测数据进行了多方案对比验证及检验。预测试验显示MGF模型具有较高的预测精度,并表现出较好的长期预测的稳定性特点。以SSA去噪序列为基础,应用MGF模型预测了各站位至2050年的月尺度海面值,年均值计算结果表明至2050年海面波动上升的幅度不超过20cm,海面变化速率同样表现出阶段性和波动性。与前人相关研究成果对比表明,本文所采用的SSA与MGF相结合的预测结果具有可比性,在方法原理和验证结果上看具有较好的长期预测潜力。