DATA CONTEXT MODEL IN THE PRO-CESS INTEGRATION FRAMEWORK

Process integration is the important aspect of product development process. The recent researches focus on project management, workflow management and process modeling. Based on the analysis of the process, product development process is divided into three levels according to different grains from macroscopy to microcosm. Our research concentrate on the workflow and the free-grained design process. According to the need of representing the data and the relationships among them for process integration, context model is introduced, and its characters are analyzed. The tree-like structure of inheritance among context model＇s classes is illustrated; The relationships of reference among them are also explained. Then, extensible markup language （XML） file is used to depict these classes. A four-tier framework of process integration has been established, in which model-view-controller pattern is designed to realize the separation between context model and its various views. The integration of applications is applied by the encapsulation of enterprise＇s business logic as distributed services. The prototype system for the design of air filter is applied in an institute.

Context Model in Chinese and American Reports on Yangtze Sunken Ship

Two pieces of news reports on Yangtze Sunken Ship event are analyzed in this essay to prove the influence of context model in news reports discourse by comparative analysis approach. Through illustrating the different expressions in these reports discourse, the process how cognition(or context model) influences and determines the production and understanding of discourse has been demonstrated.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

A hierarchical ontology context model for work-based learning

Context modelling involves a） characterizing a situation with related information, and b） dealing and stor- ing the information in a computer-understandable form. It is the keystone to enable a system to possess the perception ca- pacity and adapt its functionality properly for different situa- tions. However, a context model focusing on the characteris- tics of work-based learning is not well studied by pioneering researchers. For addressing this issue, in this work we firstly analyze several existing context models to identify the essen- tials of context modelling, whereby a hierarchical ontology context model is proposed to characterize work-based learn- ing. Subsequently, we present the application of the proposed model in work-based learning scenario to provide adapted learning supports to professionals. Hence, this work has sig- nificance in both theory and practice.

Exploiting a depth context model in visual tracking with correlation filter

Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.

Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information

With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.

Approach to Model-Based Development of Context-Aware Application

In order to address challenges posed by advances in mobile computing, ubiquitous devices software engineering, wireless and sensor technologies in many applications running, this paper provides an approach that simplifies the design and the implementation of context-aware Interactive systems called CAISDA (Context-Aware Interactive System Development Approach). This approach is based on the analysis and synthesis of context-aware frameworks proposed in literature.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

An Analysis of Discourse Production and Cognitive Context Dynamism Based on Relevance-Adaptation Model

Taken discourse production as the research objective,it holds that discourse production is dynamic in human communication.It attempts to analyze the dynamics on the basis of Relevance-adaption model from the perspective of cognitive pragmatics and explain the role of the context dynamics that plays in the discourse production.

Context Capture在车辆三维建模中的应用

无人机倾斜摄影技术和Context Capture因具有自动化程度高、建模精度和效率高等特点而被广泛应用于三维建模中。本文使用无人机倾斜摄影技术和Context Capture分别对一辆SUV和一辆三厢轿车进行了三维建模,并对其进行了精度分析。结果表明,采用多条航线的无人机拍摄策略可以充分捕捉车辆各个侧面的细节信息。使用Context Capture构建的车辆三维模型细节清晰可辨,真实地反映了车辆外形。车辆特征点实测数据与航拍数据之间的最大相对误差为0.9%,确保了车辆三维建模的精度要求。

基于Context模型的Shearlet变换地面微地震随机噪声压制

地面微地震监测采集到的微地震信号通常能量微弱,信噪比低,如何提高微震数据的信噪比是数据处理的难题.Shearlet变换是一种新型的多尺度几何分析方法,具有敏感的方向性和较强的稀疏表示特性,能起到很好的随机噪声压制效果.由于地面微震数据的有效信号大多被淹没在噪声中,基于传统阈值的Shearlet变换(the traditional threshold-based Shearlet transform TST)只考虑到尺度或方向的阈值,在去噪过程中会过度扼制有效信号系数,造成有效信号能量损失.因而,本文建立Context模型,得到基于Context模型的Shearlet变换(the Context-model-based Shearlet transform CMST)方法,改进传统Shearlet阈值方法的不足.我们通过所建立的Context模型将能量相近的各方向系数划分为同一组,并分组估计阈值,分别处理各部分系数,达到微弱同相轴有效恢复的目的.通过TST及CMST的模拟实验与实际地面微震记录处理结果对比可知,本文方法在低信噪比条件下比对比方法更加有效地恢复地面微震数据的微弱信号,随机噪声压制效果明显,在-10dB条件下,提升信噪比18.3741dB.

基于context模型的contourlet域图像去噪

在分析contourlet域系数分布特征的基础上提出了一种基于context模型的contourlet域图像去噪算法。算法的关键点在于:基于contourlet变换系数的分布特性,确定合适的去噪门限;利用context模型建立图像contourlet变换后的系数分类模型并根据分类使用不同的门限去噪。实验表明,本方法能较好地去除图像噪声,在提高去噪图像PSNR值和改善主观视觉效果方面都表现出了良好的性能。

基于Context模型的小波变换阈值自适应图像去噪

根据噪声和信号的小波系数在不同分解尺度、不同方向上高频系数的分布不同,结合Context模型,提出基于Context模型的小波变换阈值自适应图像去噪算法。该算法通过对不同尺度和方向的小波分解系数应用不同的阈值方法进行去噪。实验表明,方法能较好地去除图像噪声和保留图像边缘细节信息,在提高去噪图像信噪比值和改善视觉效果方面都表现出了良好的性能。

基于小波和Context模型的海面红外弱小目标检测

红外图像中弱小目标的检测一直是图像处理领域的热点和难点。介绍了一种基于小波变换和Context模型空间自适应滤波的红外弱小目标检测算法。该方法首先利用小波滤波器抑制大部分背景杂波,然后采用基于Context模型的空间自适应滤波器对高频系数做进一步处理,提高信噪比,最后采用基于Bayes分类算法的迭代门限进行图像分割。实验结果表明,与高通滤波相比,该方法能够更有效地检测出弱小目标。

基于提升小波和Context模型的图象无失真编码方案

文章讨论了提升小波变换的基本原理。并根据提升小波系数的特点,结合基于Context模型的熵编码,提出了一种新的无失真图象编码方案。该方案中,将提升小波系数的编码分为三部分考虑,即反映非零系数位置的SignificanceMap、反映非零系数幅值大小的MostSignificanceBits(MSB)符号流和去除了MSB的剩余二进制符号流。因为Signifi-canceMap和MSB符号流中存在较强相关性,提出了简单有效的Context模型对它们进行熵编码。而对去除了MSB的剩余二进制符号流,由于其相关性较弱,直接使用自适应算术编码。经过实验对比证明了该方案在无失真图象编码中的有效性。

基于Context模型的ECG信号二维压缩

提出一种基于Context模型的ECG信号二维压缩方案.通过模极大检测和循环匹配识别R波特征,自动构建ECG图像,并根据心动周期信息制作编码数据图,之后对ECG图像进行一维离散小波变换和带截止区均匀量化,量化系数被分解为重要位置图、符号流、最高位位置流和剩余比特流,最后结合编码数据图进行基于Context模型的自适应算术编码.实验针对MIT-BIH心律失常数据库的两个数据集进行压缩.压缩比为20时,新方案的百分均方根误差分别为2.93%、4.31%,低于JPEG2000压缩方案的3.26%、4.8%.结果表明新方案优于其它ECG压缩算法.

应用小波域三维Context模型的视频图像去噪

考虑视频图像序列的各帧之间具有较强的相关性,提出了一种基于三维小波变换和分块Context模型的视频去噪新方法(3DWTBCM)。3DWTBCM法基于视频图像三维小波分解域内系数和噪声分布的特征,利用小波系数具有局部相关性对其进行分块,将系数分解成各个局部区域。然后,将Context模型用于局部块中,按照能量分布将块内的小波系数分成多个子块。对各部分进行能量估计和多阈值估计,获得去噪最佳阈值,并有效地消除噪声。实验结果表明,3DWTBCM的噪声抑制效果明显优于各种2D去噪方法和其他常用的3D去噪声方法,PSNR平均提高0.5～1.2dB。而且从视觉效果来看,本文算法在去除噪声的同时,较好地保留了运动图像细节,运动物体显得比较平滑,不存在传统算法中的拖影、闪烁等现象。