基于数字高程模型的建筑物自动提取算法研究

从遥感影像中自动提取建筑物是研究从遥感影像构建地图的关键技术。针对当前建筑物自动提取算法中存在的缺陷,提出了一种基于数字高程模型的建筑物自动提取算法。该算法是建立在灰度数学形态学、矢量化变换、离散化噪音消除以及边缘简化技术综合的基础上,充分考虑了建筑物后续重建计算量的问题。实验结果表明利用该算法提取出的建筑物边缘清晰正确,同时又能大大减少冗余数据量。     

Test data generation based on a formal analysis of the project configuration data

A method of preparation of the test data that provides a specified level of coverage of the requirements for functional testing is proposed. The application of the method simplifies the maintenance of the project life cycle data configuration, which includes the requirements, source code, and generated tests, in a consistent state. A classification of software defects is introduced. An approach to formalization of the analysis requirement and implementation of the tested system based on the presentation of a partition into classes of equivalency in the form of a logic equation system is proposed. An approximate method of solving the acquired equations is proposed. The applicability of the method in real processes of industrial projects is discussed.     

Assessment of Digital Elevation Model (DEM) aggregation methods for hydrological modeling: Lake Chad basin,Africa

Digital Elevation Models (DEMs) are used to compute the hydro-geomorphological variables required by distributed hydrological models. However, the resolution of the most precise DEMs is too fine to run these models over regional watersheds. DEMs therefore need to be aggregated to coarser resolutions, affecting both the representation of the land surface and the hydrological simulations. In the present paper, six algorithms (mean, median, mode, nearest neighbour, maximum and minimum) are used to aggregate the Shuttle Radar Topography Mission (SRTM) DEM from 3″ (90 m) to 5′ (10 km) in order to simulate the water balance of the Lake Chad basin (2.5 Mkm²). Each of these methods is assessed with respect to selected hydro-geomorphological properties that influence Terrestrial Hydrology Model with Biogeochemistry (THMB) simulations, namely the drainage network, the Lake Chad bottom topography and the floodplain extent.The results show that mean and median methods produce a smoother representation of the topography. This smoothing involves the removing of the depressions governing the floodplain dynamics (floodplain area<5000 km²) but it eliminates the spikes and wells responsible for deviations regarding the drainage network. By contrast, using other aggregation methods, a rougher relief representation enables the simulation of a higher floodplain area (>14,000 km² with the maximum or nearest neighbour) but results in anomalies concerning the drainage network. An aggregation procedure based on a variographic analysis of the SRTM data is therefore suggested. This consists of preliminary filtering of the 3″ DEM in order to smooth spikes and wells, then resampling to 5′ via the nearest neighbour method so as to preserve the representation of depressions. With the resulting DEM, the drainage network, the Lake Chad bathymetric curves and the simulated floodplain hydrology are consistent with the observations (3% underestimation for simulated evaporation volumes).     

Machine learning methods for sub-pixel land-cover classification in the spatially heterogeneous region of Flanders (Belgium): a multi-criteria comparison

Until now, few research has addressed the use of machine learning methods for classification at the sub-pixel level. To close this knowledge gap, in this article, six machine learning methods were compared for the specific task of sub-pixel land-cover extraction in the spatially heterogeneous region of Flanders (Belgium). In addition to the classification accuracy at the pixel and the municipality level, three evaluation criteria reflecting the methods’ ease-of-use were added to the comparison: the time needed for training, the number of meta-parameters, and the minimum training set size. Robustness to changing training data was also included as the sixth evaluation criterion. Based on their scores for these six criteria, the machine learning methods were ranked according to three multi-criteria ranking scenarios. These ranking scenarios correspond to different decision-making scenarios that differ in their weighting of the criteria. In general, no overall winner could be designated: no method performs best for all evaluation scenarios. However, when both time available for preprocessing and the magnitude of the training data set are unconstrained, Support Vector Machines (SVMs) clearly outperform the other methods.     

基于容器(CUP)算法的斑图生成和分析

斑图生成是一个具有生物学背景的复杂系统问题,相比传统的动力学等方法,采用CUP算法进行斑图的生成和分析。同时CUP算法具有容易并行、解释方式多样(概率,结构化)、具有多尺度求解和解释能力、参数多样化和结构多样化等优点,其斑图应用对复杂系统的研究分析有实际的指导意义。     

Multidimensional visualisation for process historical data analysis: a comparative study with multivariate statistical process control

This paper describes a comparative study of a multidimensional visualisation technique and multivariate statistical process control (MSPC) for process historical data analysis. The visualisation technique uses parallel coordinates which visualise multidimensional data using two dimensional presentations and allow identification of clusters and outliers, therefore, can be used to detect abnormal events. The study is based on a database covering 527 days of operation of an industrial wastewater treatment plant. It was found that both the visualisation technique and MSPC based on T² chart captured the same 17 days as “clearly abnormal” and another eight days as “likely abnormal”. Pattern recognition using K-means clustering was also applied to the same data in literature and was found to have identified 14 out of the 17 “clearly abnormal” days.     

Generation of a top‐of‐canopy Digital Elevation Model (DEM) in tropical rain forest regions using radargrammetry

A feasibility study is prepared for creating a top‐of‐canopy Digital Elevation Model (DEM) based on Radarsat data from the tropical rain forest regions of Malaysia. Through a case study, the accuracy of the generated DEM is verified, showing reasonable accuracy.     

基于历史数据挖掘和劳斯判据的某火电机组功率振荡机理分析

当汽轮机阀门流量特性恶化时,会引起发电机组功率振荡,进而可能引发电网低频振荡。本文以发生过功率振荡的某660MW超临界机组为例,通过数据挖掘和劳斯稳定判据原理,挖掘机组历史数据得到实际阀门流量特性,同时建立有功调节系统综合模型,定性、定量地分析汽轮机阀门流量特性对系统稳定性的影响,计算得到使系统失稳的阀门流量特性曲线曲率（k）的临界值为3.3438,当k大于临界值时,系统将失稳。同时,通过数据挖掘分析发现机组在92.7%～96.6%综合阀位指令段的阀门流量特性恶化严重,k值最大达到了7.771,远大于临界值,这是导致机组在该指令区间内发生多次功率振荡事件的根本原因。本文的分析方法对今后类似功率振荡事件的分析诊断,有一定的借鉴作用。     

Model checking Trampoline OS: a case study on safety analysis for automotive software

Model checking is an effective technique used to identify subtle problems in software safety using a comprehensive search algorithm. However, this comprehensiveness requires a large number of resources and is often too expensive to be applied in practice. This work strives to find a practical solution to model‐checking automotive operating systems for the purpose of safety analysis, with minimum requirements and a systematic engineering approach for applying the technique in practice. The paper presents methods for converting the Trampoline kernel code into formal models for the model checker SPIN, a series of experiments using an incremental verification approach, and the use of embedded C constructs for performance improvement. The conversion methods include functional modularization and treatment for hardware‐dependent code, such as memory access for context switching. The incremental verification approach aims at increasing the level of confidence in the verification even when comprehensiveness cannot be provided because of the limitations of the hardware resource. We also report on potential safety issues found in the Trampoline operating system during the experiments and present experimental evidence of the performance improvement using the embedded C constructs in SPIN. Copyright © 2012 John Wiley & Sons, Ltd.     

基于时间自动机模型的无线传感器网络数据收集协议测试用例生成

从协议测试的角度出发研究了基于时间自动机模型的无线传感器网络数据收集协议测试用例生成方法,提出利用UPPAAL工具对数据收集协议建立时间自动机模型,进而利用UPPAAL CoVer工具生成满足特定覆盖标准的测试路径集合的方法。为了便于生成实际测试用例,开发了辅助自动测试用例生成工具ATCGT。通过一个工业界的无线抄表数据收集协议的建模与测试用例生成实例,阐明了该方法的有效性。     

A feature analysis for dimension reduction based on a data generation model with class factors and environment factors

Currently, high-dimensional data such as image data is widely used in the domain of pattern classification and signal processing. When using high-dimensional data, feature analysis methods such as PCA (principal component analysis) and LDA (linear discriminant analysis) are usually required in order to reduce memory usage or computational complexity as well as to increase classification performance. We propose a feature analysis method for dimension reduction based on a data generation model that is composed of two types of factors: class factors and environment factors. The class factors, which are prototypes of the classes, contain important information required for discriminating between various classes. The environment factors, which represent distortions of the class prototypes, need to be diminished for obtaining high class separability. Using the data generation model, we aimed to exclude environment factors and extract low-dimensional class factors from the original data. By performing computational experiments on artificial data sets and real facial data sets, we confirmed that the proposed method can efficiently extract low-dimensional features required for classification and has a better performance than the conventional methods.     

Timed-automata based schedulability analysis for distributed firm real-time systems: a case study

The growing level of complexity of modern embedded systems, which are increasingly distributed and heterogeneous, calls for new design approaches able to reconcile mathematical rigour with flexibility, user-friendliness and scalability. In the last few years, Timed Automata (TA) have emerged as a very promising formalism for the availability of very effective verification tools. However, their adoption in the industrial community is still slow. The reluctance of industrial practitioners is partly motivated by persistent concerns on the ease of use of this formalism, on the scalability of the verification process and on the quality of the feedback that the designer receives. In this paper, we discuss these issues by showing the application of the TA formalism on a case study of industrial complexity. We expose the generality of the approach, the efficiency of state of the art tools, but also the limitations in the semantics and in dealing with free design parameters.     

Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis does not provide an analytical means to determine the importance of the identified factors or the ability to assess decision alternatives according to these factors. Although the analysis successfully pinpoints the factors, individual factors are usually described briefly and very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation steps. Although the analytic hierarchy process (AHP) technique removes these deficiencies, it does not allow for measurement of the possible dependencies among the factors. The AHP method assumes that the factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain internal and external environmental effects. Therefore, it is necessary to employ a form of SWOT analysis that measures and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities.     

Big data and sentiment analysis to highlight decision behaviours: a case study for student population

ABSTRACT

Starting from the assumption that the factors orienting University choice are heterogeneous and multidimensional, the study explores student’s motivations in higher education. To this aim, a big data analysis has been performed through ‘TalkWalker’, a tool based on the algorithms developed in the context of Social Data Intelligence, which allows understanding the sentiment of a group of people regarding a specific theme. The data have been extracted by drawing on published posts from anywhere in the world over a 12-month period from many online sources. According to the findings, the main variable capable of influencing the choice of University is training offer, followed by physical structure, work opportunities, prestige, affordability, communication, organisation, environmental sustainability. The study establishes an innovative research agenda for further studies by proposing the elaboration of a systems and process-based view for higher education. However, it presents the limitation of the superficial investigation, determined by the analysis of a large amount of data. Therefore, for future research, it might be appropriate to apply a different technique to realise a comparison and to check whether the size of the considered sample and the depth of the analysis technique can affect the results and the consequent considerations.     

基于可拓理论的CBR系统实例检索模型

围绕产品配置设计中实例检索的准确性问题,依据可拓学中关联函数的概念,提出了一种新的实例检索方案。根据客户定制需求和产品实例对属性的描述方式对属性进行分类,在可拓学关联函数的基础上分别定义它们的相似度的计算方法,根据AHP（Analytical Hierarchy Process）计算属性的权重比,给出客户需求与产品实例的相似度计算模型,并通过一个具体例子证明模型的有效性。     

Evaluating temporal changes using Multi-Spectral Scanner and Thematic Mapper data on the landscape of a natural reserve: The New Jersey Pine Barrens,a case study

Natural disturbance suppression and anthropogenic perturbations have altered the composition and structure of the New Jersey Pinelands National Reserve (NJPNR). The combination of satellite remote sensing imagery and GIS provided the means to map and monitor land cover change at landscape level scales in the NJPNR. The Pinelands has experienced a change in landcover, with the mixed deciduous forest replacing the pine forest community.     

An analysis of changes in a multi-date data set: A case study in the Ikitelli area,Istanbul, Turkey

The analysis of changes has become an important use of multi-spectral Landsat TM data. With the repetitive acquisition of imagery, it is possible to determine the types and extent of changes in an environment. Many digital change-detection techniques, such as image overlay, image differencing, and principal component analysis, have been used widely for this purpose. The objective of this study was to detect land cover changes by using the multi-date Landsat TM imagery for the Ikitelli area, Istanbul, Turkey, employing different methods. Each change detection method used was assessed, with its ability to detect specific changes.     

Observability analysis of nonlinear tubular (bio)reactor models: a case study

This paper deals with the observability analysis of nonlinear tubular bioreactor models. Due to the lack of tools for the observability analysis of nonlinear infinite-dimensional systems, the analysis is performed on a linearized version of the model around some steady-state profile, in which coefficients can be functions of the spatial variable. The study starts from an example of tubular bioreactor that will serve as a case study in the present paper. It is shown that such linear models with coefficients dependent on the spatial variable are Sturm–Liouville systems and that the associated linear infinite-dimensional system dynamics are described by a Riesz-spectral operator that generates a C₀ (strongly continuous)-semigroup. The observability analysis based on infinite-dimensional system theory shows that any finite number of dominant modes of the system can be made observable by an approximate point measurement.     

A new interval type-2 fuzzy approach for analyzing and monitoring the performance of megaprojects based on earned value analysis (with a case study)

Neural Computing and Applications - Major factors of project success include using tools of performance measurements and feedbacks. Earned value management (EVM) is a unique issue within...     

The governance strategies for public emergencies on social media and their effects: a case study based on the microblog data

Since the 1980s, the development of information and communication technologies (ICTs) has greatly changed people’s modes of production and lifestyle, and it has also had a significant influence on traditional social structures. Microblogs – a type of social media application such as Twitter or Weibo – have served as an important platform for network governance in some local governments in China. This study makes an attempt to answer the following questions: What types of strategies should governments implement on social media platforms during public emergencies? What are the effects of these strategies? Based on the case of the Shifang Incident, which was a large-scale environmental protest that occurred in Shifang, China in 2012, we analyze all the messages posted during the incident on the official microblog of the Shifang government and examine the public feedback by using an online big data analysis tool. In line with the time sequence and the extent of the conflict, we divide the Shifang Incident into three phases: the fermentation period, the confrontation period, and the digestion period. In addition, we classify government strategies on social media into five categories: introducing, appealing, explaining, rumor-refuting, and decision-making. The analysis results show that different government strategies are applied to different phases of the incident and that the responses of the public also vary during different periods.