基于DMSP/OLS数据的城市扩张研究

为研究太原市城市扩张动态变化,采用DMSP/OLS(Defence Meteorological Satelite Program/Oprational Linescan System)夜间灯光数据结合统计数据法提取城市建成区,提出加权重心转移法提取城市重心,并从用地扩张时空特征、城市扩张类型、基于灯光强度的城市重心迁移几个方面展开研究。结果表明,太原市城市建成区持续扩张,且城市重心不断向南移动。1992~2002年,太原市主要扩张走势为向西以及西南方向扩张,为填充增强主导扩张类型;2002~2012年扩张强度尤其明显,主要扩张走势为正南、东南方向扩张,为外延主导扩张类型。

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

Using Statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data

Regional Landslide Susceptibility Zonation(LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression(LR), Artificial Neural Networks(ANN) and Support Vector Machine(SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis(LDA), receiver operating characteristic(ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadilyincreasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.

Regression-Based Artificial Neural Network Methodology in Response Surface Methodology

Response surface methodology (RSM) is an important tool for process parameter optimization, robust design and other quality improvement efforts. When the relationship between influential input variables and output response is very complex, it’s hard to find the real response surface using RSM. In recent years artificial neural network(ANN) has been used in RSM. But the classical ANN does not work well under the constraints of real applications. An algorithm of regression-based ANN(R-ANN) is proposed in this paper, which is a supplement to the classical ANN methodology. It makes network closer to the response surface, so that training time is reduced and robustness is strengthened. The procedure of improving ANN by regressions is described and the comparisons among R-ANN,RSM and classical ANN are computed graphically in three examples. Our research shows that the R-ANN methodology is a good supplement to the RSM and classical ANN methodology, which can yield lower standard error of prediction under conditions that the scope of experiment is rigidly restricted.

International Accounting Standards Board （IASB） and Financial Accounting Standards Board （FASB） Convergence Project： Where Are They Now？

The convergence project between the International Accounting Standards Board （IASB） and the Financial Accounting Standards Board （FASB） in the United States （US） was signed on September 18, 2002 in Norwalk, Connecticut in the US. The first is responsible for issuing International Financial Reporting Standards （IFRS） nowadays, which were created 40 years ago. More than one century ago, local regulations are used in the US. The boards differ in years of experience. With the signing of the agreement, both institutions are working to reduce the divergence of accounting. Although they have made a significant progress, it is appropriate to examine whether the difference in approaches to accounting will affect the achieved agreements. It is relevant to investigate whether the years leading the standards adopted in different countries will impact the final result. The date of completion of the project has been postponed and still has not indicated the date of termination. This research is an analysis of the importance of the convergence of accounting standards at a global level. The study presents statistics on the status of the adoption of international standards by country. The study shows a summary of the expressions made by the directors of both boards about the future of the project.

The Demographic Impact on Credit Scores＂ Evidence From Statistical Methods and Geographic Information Systems （GIS） Mapping

Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loan and employment decisions, people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions. Although credit scores are the direct result of credit histories, credit histories may be impacted by demographic factors. If the demographic factors that impact credit histories are identified, ways to improve credit scores are likely to be discovered and available to people and state government policymakers. This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US. The methodology includes statistical analyses and geographic information systems （GIS） mapping. Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education, family, income, and health. GIS mapping reveals clusters of states with similar demographics and credit scores.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Landslide Hazard Mapping During a Large Scale Earthquake

This paper reports a method to make hazard maps of sediment disasters resulting from an earthquake and following heavy rainfall for the entire region of Gunma prefecture, Japan. Firstly, we identified the slopes in the study area, which are susceptible to large-scale landslides and land failures during an earthquake with a magnitude of seven on the Richter scale. To analyze the sheer volume of the data, we employed a statistical method to evaluate the susceptibility, mainly considering geomorphologic conditions. Secondly, we extracted mudflow and slope failure susceptible areas and potential flooding zones resulting from a damming at a river triggered by the earthquake and heavy rainfall, and we identified the settlements which would be isolated by the road disruption caused by the sediment disasters. As the result, 359 settlements were classified as potential isolation areas. Combining the above-mentioned susceptibility maps, we obtained two types of sediment disaster hazard maps of the study area, depicting the potential hazards which would occur during the earthquake and the disasters which would be caused by heavy rainfall following the quake, respectively. These hazard maps and the disaster information would be useful for the regional disaster prevention planning and countermeasures in the future.

Analysis of the Synoptic Situation Field on the Red Tide Occurred in Yangtze River Estuary Water Area

The occurrence of the red tide is an extremely complex process, which is considered as the comprehensive result of various factors. The Yangtze River estuary water area is always in high incidence area of red tide. In this paper, according to the events of red tide and meteorologic and hydrologic data in Yangtze River estuary water area from 2000 to 2010, by using mathematical statistics methods, we analyze the relevance between the occurrence of the red tide and the synoptic situation field, and probe into the regular patterns.

An Analysis on the Spatial Distribution of Population Aging Pressure in China

Based on comprehensive analysis of the impact of population aging to social and economic development,a comprehensive evaluation system including 18 indexes was constructed for evaluating regional pressure of population aging on social and economic development.Using statistics data of 31 regions in China from 2004 to 2008,the pressure of population aging on social and economic development,was comprehensively evaluated by using the factor analysis method.The spatial distribution of population aging in China was also analyzed.This study is to provide scientific basis for government to make strategies of coping with population aging according to regional pressure of population aging on social and economic development in China.

Mining Frequent Closed Itemsets in Large High Dimensional Data

Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining.To solve the problem,a hybrid method,consisting of a novel row enumeration algorithm and a column enumeration algorithm,is proposed.The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively.The novel algorithm,i.e.,Inter-transaction is based on the characteristic that there are few common items between or among long transactions.In addition,an optimization technique is adopted to improve the performance of the intersection of bit-vectors.Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.

A Survey of the Field of Hazard and Disaster Studies

This paper examines trends in the impacts of disasters at the world scale and proposes explanations for them. It is concluded that statistics on natural hazards are questionable, in that changes in recording methods have artificially inflated the number of disasters and the seriousness of their impacts. Next, the paper examines the symbolic interpretation of disasters at various scales of analysis in terms of human cultures. It further applies this approach to the analysis of natural hazards using information technology methods such as GIS （Geographic Information Systems）. Returning to the global scale, deficiencies and inequalities in the world disaster relief system are considered. Thereafter, the paper investigates how global economic imbalances are thrown into sharp relief by catastrophes, with examples from the record of past earthquakes, landslides and floods. Finally, it considers the prospects for a major change in the direction of world policy on disasters and concludes that this will only occur if the international financial system is forced to adjust to a ＂super-disaster＂ event, which could possibly be seismic, volcanic or nuclear in origin.

State Vectors—A New Method for Seismicity Analysis

The concept of state vector stems from statistical physics, where it is usually used to describe the evolution of a continuum field in its way of coarse-graining. In this paper, the state vector is employed to depict the evolution of seismicity quantitatively, and some interesting results are presented. The authors investigated some famous earthquake cases (e.g., the Haicheng earthquake, the Tangshan earthquake, the west Kunlun Mountains earthquake, etc.) and found that the state vectors evidently change prior to the occurrence of large earthquakes. Thus it is believed that the state vector can be used as a kind of precursor to predict large earthquakes.

TeachingmA Way of Implementing Statistical Methods for Ordinal Data to Researchers

The use of questionnaires, rating scales and other kinds of ordered classifications is unlimited and interdisciplinary, so it can take long time before novel statistical methods presented in statistical journals reach researchers of applied sciences. Therefore. teaching is an effective way of introducing novel methods to researchers at an early stage. Assessments on scales produce ordinal data having rank-invariant properties only, which means that suitable statistical methods are non-parametric and often rank-based. These limited mathematical properties have been taken into account in the research regarding development of statistical methods for paired ordinal data. The aim is to present a statistical method for paired ordinal data that has been successfully implemented to researchers from various disciplines together with statisticians attending interactive problem solving courses of biostatistics.

The Strict Swedish vs. the Loose Dutch System for Regulations on Prostitution and Drug Use

The author＇s research topics include economic and legal questions concerning prostitution and drug use. There are two extreme models in these two areas, one represented by Sweden and the other by the Netherlands. Sweden votes for the model of a ＂prostitution and drug free society＂, while the latter represents a looser, more liberal view. This presentation aims to answer the question whether the statistics support the presumptions of either model. To shed light on these issues, facts and figures published by the Swedish and Dutch statistics offices as well as other studies on the subject were analyzed. During the course of the past few decades. the income from the prostitution and drug markets in Sweden was virtually unchanged. This may be considered as a failure： prostitution and drug use could not be decreased further. It may also be considered as an achievement： while these two markets were growing in many countries, at least in the case of Sweden the situation did not become worse. It seems that the liberal regulation of drugs and prostitution in the Netherlands has not been a successful venture. It is no wonder that there are plans for changes and limitations to the prostitution sector with new regulations, although complete prohibition has not been raised as an issue. The consumption of drugs has been increasing. In this respect, alongside the liberal stance on the issue, increasing emphasis is placed on deterrence from light drugs.

Design and Implementation of English Teaching Management System Based on Web

In order to improve the intelligence of the English teaching management, so as to improve the English teaching resource scheduling and sharing ability, a design method of teaching management system is proposed based on Web. The system adopts Web network architecture design, mainly it is divided into MUC main control module, database module, data management module and man-machine interaction module, Network management and online scheduling of English teaching resources are obtained based on C/S framework, data exchange of English teaching resources is realized on client side and server side, the control component of the English teaching resource distribution database uses MBM29LV400BC as the logic decoding controller. The intelligent algorithm is used to analyze the big data to realize the scheduling and access control of the English teaching management system resources. The test results show that the design of the English teaching management system has good resource scheduling and database construction ability, and the overall intelligence of the system is better.

Sorting Data Elements by SOCD Using Centralized Diamond Architecture

Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.

Towards an Egyptian Mobile Banking Era

Banks have moved towards technology-based customer services such as mobile banking （m-banking）; as an alternative to traditional inter-personal services. However, M-banking still faces resistance that may hamper customers＇ adoption in Egypt. Following a mixed method approach, this study involves two main phases. First, a quantitative Consumer survey is conducted in the form of 350 structured questionnaires to identify the main barriers towards m-banking. Then, decision makers in 5 different public and private banks are interviewed in order to find similarities and difference, and identify service providers＇ roles regarding these barricrs. Questionnaire data was statistically analysed using independent T-Test, Chi-squares, frequencies and cross tabulations, while interview data was interpretively analysed. Results enabled the researchers to identify the risk, usage, and tradition as the most m-banking barriers that discourage m-banking adoption in Egypt as seen by the two main players.

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.

Analysis on clinically drug-used law for lung-intestine related diseases

OBJECTIVE： To analyze the application laws of clini- cal drugs by descriptive statistical method with the clinical literatures guided by ＂lung being connect- ed with large intestine＂ used as the object. METHODS： Retrieve publicly published clinical litera- tures about ＂lung being connected with large intes- tine＂ in recent 30 years, establish the data base of clini- cal literature, and study on the clinically drug-used laws by descriptive statistical analytic method. RESULTS： The common high frequent drugs used for the lung-intestine related diseases were Gualou （Ructus Trichsanthis）, Dahuang （Radix et Rhizoma）, Kuxingren （Semen Armeniacae Amarum）, Gancao （Radix Glycyrrhizae）, Houpu （Cortex Magnoliae Offi- cinalis）, Zhishi （Fructus Aurantii Immaturus）. For the different clinical syndrome types, in the commonly used drugs there were different characteristics, re- flecting the principle of TCM syndrome differentia- tion treatment.CONCLUSION： The common high frequent drugs used for the lung-intestine related diseases are Gua- Iou （Fructus Trichsantffis）, Dahuang （Radix et Rhi- zoma）, Kuxingren （Semen Armeniacae Amarum）, Gancao （Radix Glycyrrhizae）, Houpu （Cortex Mag- noliae Officinalis）, Zhishi （Fructus Aurantii Immatu- rus）, which are commonly-used drugs for the lung-intestine related diseases ,with differences for different syndrome types.