An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

An Effective Error Resilient Packetization Scheme for Progressive Mesh Transmission over Unreliable Networks

When a 3D model is transmitted over a lossy network,some model information may inevitably be missing. Under such situation,one may not be able to visualize the receiving model unless the lost model information has been retransmitted. Progressive model transmission offers an alternative to avoid the "all or nothing situation" by allowing a model to be visualized with a degraded quality when only part of the model data has been received. Unfortunately,in case some model refinement information is missing,one may still need to wait for such information to be retransmitted before the model can be rendered with a desired visual quality. To address this problem,we have developed a novel error resilient packetization scheme. We first construct a Non-Redundant Directed Acyclic Graph to encode the dependencies among the vertex splits of a progressive mesh. A special Global Graph Equipartition Packing Algorithm is then applied to partitioning this graph into several equal size sub-graphs,which is packed as packets. The packing algorithm comprises two main phases:initial partition phase and global refinement phase. Experimental results demonstrate that the proposed scheme can minimize the dependencies between packets. Hence,it reduces the delay in rendering 3D models with proper quality at the clients.     

An object-oriented knowledge representation for spatial information

An abstract formalism for the representation of spatial knowledge is suggested. The focus is on the development of a comprehensive representation scheme for pictorial information in which the knowledge model of the given world has a high degree of perceptual similarity to a typical user's view of the same world. The model that has been developed uses the object-oriented method of knowledge representation. The intention is that with this model any user of the system will be equipped to depict pictorial information easily and will be able to portray spatial as well as conceptual abstractions, generalizations, and rules at various levels     

Model reduction in the presence of uncertainty in model parameters

This short note describes how to extend a certain class of existing model reduction techniques to take into account uncertainty in model parameters. The key idea of this extension is that the reduced-order model should not only contain the model parameters, but that the reduction procedure itself has to be geared for dealing with parametric uncertainty. This goal is achieved by augmenting the vector of inputs to the system with the uncertain parameters and by performing model reduction on the augmented system. It is shown that error bounds for the reduced-order model can be computed if the underlying system is linear with respect to the states, parameters, and inputs. A comparison between the presented technique and a conventional approach is made via two examples.     

Towards an Intelligent Tutoring System Architecture that Supports Remedial Tutoring

For successful teaching to take place an intelligenttutoring system has to be able to cope with anystudent errors that may occur during a tutoringinteraction. Remedial tutoring is increasingly viewedas a central part of the overall tutoring process, andrecent research calls for adaptive remedial tutoring. This paper discusses the issues of remedial tutoringthat have been proposed or implemented to supportefficient remedial tutoring. These issues serve touncover any underlying principles of remediation thatgovern remedial tutoring with intelligent tutoringsystems. In order to incorporate these principles ofremediation into intelligent tutoring systemsdevelopment processes this paper continues with thedevelopment of a model that can be employed in thedevelopment of an intelligent tutoring system that iscapable of offering remedial tutoring according tothese principles. This model is a formalisation ofremedial interventions with intelligent tutoringsystems. To demonstrate how the model can be employed indeveloping an intelligent tutoring system, INTUITION,the implementation of an existing business simulationgame, has been developed. This paper concludes with anillustration of how the model for remedial operationsprovides for remedial tutoring within INTUITION. Theevaluation of INTUITION shows that the model forremedial operations is a useful method for providingefficient remedial tutoring.     

Aesthetic and functional analysis for product model validation in reverse engineering applications

Reverse engineering techniques are broadly used in the development process of products with meaningful aesthetic properties. Many stylists prefer to evaluate the product shape on the basis of a full-scale hand made physical mock-up. Such model has then to be converted into a 3D CAD model, to begin the product engineering and production processes.A critical aspect of the RE process is that the physical mock-up, made by the stylist, usually does not take into account the engineering production constraints. The surface reconstruction activity must then be followed by a modelling phase, where the engineering designer modifies the reconstructed model shape in order to make it suitable for production.At the end of this process, no matter how accurate and precise the surface reconstruction phase has been, the product model will be different from the original mock-up, and then it needs to be submitted to the stylist for the validation of its shape.For the stylist, getting used to working with physical models, the shape validation on a virtual model is not a trivial task.The objective of this research is to develop a software tool to support the identification of aesthetic and functional regions of the product model shape, that have been modified with respect to the original mock-up.The proposed approach is based on the decomposition of a 3D surface analysis problem into a simpler 2D curves analysis problem. This approach simulates the traditional method used by stylists to evaluate the quality of shapes.The implementation of the developed algorithms has been performed using a commercial software package (I-deas FreeForm by EDS/Unigraphics) and it has been successfully applied on real test cases.     

基于气体传感器动力学模型的细菌分类研究

对于气体传感器获得的样本数据,常规的处理方法是基于样本的表象来提取特征进行分类,具有固有的局限性.基于热电子发射理论和等温吸附理论,对气体传感器的电压响应值与温度、样本浓度等参数建立数学方程,将方程简化,构造了简易的响应动力学模型.通过主成分分析,降低样本维数.将模型向降维后的样本数据拟合,可得到模型的一组系数,作为样本的特征值.将特征值集合运用模式识别方法进行训练,测试分类性能.实验结果显示:分类预测的准确率较高.     

一种改进的基于记忆的自适应汉语语言模型

基于记忆的自适应语言模型虽然在一定程度上增强了语言模型对不同领域的适应性,但其假设过于简单,即认为一个在文章的前面部分出现过的词往往会在后面重复出现。通过对一些文本的观察分析,我们认为作者在书写文章的时候,除了常常使用前文中出现过的词汇外,为了避免用词单调,还会在行文过程中使用前文出现过词汇的近义词或者同义词。另外,一篇文章总是围绕某个主题展开,所以在文章中出现的许多词汇往往在语义上有很大的相关性。我们对基于记忆的语言模型进行了扩展,利用汉语义类词典,将与缓存中所保留词汇语义上相近或者相关的词汇也引入缓存。实验表明这种改进在很大程度上提高了原有模型的性能,与n元语言模型相比困惑度下降了4011% ,有效地增强了语言模型的自适应性。     

Development of an image-based model for capillary vasculature of retina

The paper presents a method of development of a detailed network model to represent retinal capillary vasculature. The capillary model is a circular mesh consisting of concentric rings with an increasing diameter. Each of the rings has uniformly distributed bifurcation nodes to represent capillary vessels. The model is customized using the data that has been measured from confocal microscopic images of a mouse retina. The capillary model developed can be connected to networks of larger vessels of the vasculature such as arterial and venous networks to form a complete model of the retinal network. A method to automate such interface connections between capillary and other vascular networks using connecting vessels (i.e., pre-capillary and post-capillary) is also presented in the paper. Such a detailed image-based capillary model together with the arterial and venular networks can be used for various circulation simulations to obtain accurate information on hemodynamic quantities such as the spatial distribution of pressure and flow in the vasculature for both physiological and pathological conditions. The method presented for the development of the capillary model can also be adopted for vasculatures of other organs.     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

Coupling climate and hydrological models: Interoperability through Web Services

Understanding regional-scale water resource systems requires understanding coupled hydrologic and climate interactions. The traditional approach in the hydrologic sciences and engineering fields has been to either treat the atmosphere as a forcing condition on the hydrologic model, or to adopt a specific hydrologic model design in order to be interoperable with a climate model. We propose here a different approach that follows a service-oriented architecture and uses standard interfaces and tools: the Earth System Modeling Framework (ESMF) from the weather and climate community and the Open Modeling Interface (OpenMI) from the hydrologic community. A novel technical challenge of this work is that the climate model runs on a high performance computer and the hydrologic model runs on a personal computer. In order to complete a two-way coupling, issues with security and job scheduling had to be overcome. The resulting application demonstrates interoperability across disciplinary boundaries and has the potential to address emerging questions about climate impacts on local water resource systems. The approach also has the potential to be adapted for other climate impacts applications that involve different communities, multiple frameworks, and models running on different computing platforms. We present along with the results of our coupled modeling system a scaling analysis that indicates how the system will behave as geographic extents and model resolutions are changed to address regional-scale water resources management problems.     

A simulation model for studies of intestine cell dynamics

A dynamic simulation model for the epithelial cell structures of the intestine has been developed. The model is based on hypotheses found in the literature. It is shown that basic cell dynamics can be reproduced by simulation experiments. The simulation program is designed so that model output comparable with experimental findings can be obtained. The use of a dynamic simulation model can be interpreted as a simulated experiment in vivo. The model therefore allows analysis of dynamic behaviour that cannot be performed by normal experimental work. This means that the model can be used as a tool for verification/falsification of hypotheses about dynamics in both normal and abnormal cell structures.     

A latent Beta-Liouville allocation model

There has been a constant desire for proposing new machine learning approaches for count data modeling. One of the most referred approaches is the latent Dirichlet allocation (LDA) model (Blei et al., 2003b). LDA has been shown to be a reliable model for count data classification. It is based, however, on the consideration of the Dirichlet distribution, as a prior, which modeling capabilities have been challenged recently and some alternative priors have been proposed. One of these priors is the Beta-Liouville (BL) distribution that we will consider in this work to provide an alternative to the LDA model. In order to maintain consistency with the original model we shall call our resulting model, latent Beta-Liouville allocation (LBLA). Like the LDA, the LBLA model uses a variational Bayes method for learning its hidden parameters. It will be shown that LDA is a special case of the LBLA model that we will show its merits, in comparison to the LDA model, via three distinct challenging applications namely text classification, natural scene categorization, and action recognition in videos. We will show that the LBLA model results in improved modeling accuracy in return for a slight increase in computational complexity. We conclude that our model can be considered as a more efficient replacement for the LDA model.     

A factor-based definition of precedential constraint

This paper describes one way in which a precise reason model of precedent could be developed, based on the general idea that courts are constrained to reach a decision that is consistent with the assessment of the balance of reasons made in relevant earlier decisions. The account provided here has the additional advantage of showing how this reason model can be reconciled with the traditional idea that precedential constraint involves rules, as long as these rules are taken to be defeasible. The account presented is firmly based on a body of work that has emerged in AI and Law. This work is discussed, and there is a particular discussion of approaches based on theory construction, and how that work relates to the model described in this paper.     

A software product certification model

Certification of software artifacts offers organizations more certainty and confidence about software. Certification of software helps software sales, acquisition, and can be used to certify legislative compliance or to achieve acceptable deliverables in outsourcing. In this article, we present a software product certification model. This model has evolved from a maturity model for product quality to a more general model with which the conformance of software product artifacts to certain properties can be assessed. Such a conformance assessment we call a ‘software product certificate’. The practical application of the model is demonstrated in concrete software certificates for two software product areas that are on different ends of the software product spectrum (ranging from a requirements definition to an executable). For each certificate, a concrete case study has been performed. We evaluate the use of the model for these certificates. It will be shown that the model can be used satisfactorily for quite different kinds of certificates.     

新型线圈发射器的电磁场仿真分析

电磁发射拥有许多化学发射不可比拟的优点,近年来受到各国的广泛关注。传统发射器需在线圈内部加装位置传感器,这使得线圈磁场分布不均且结构复杂。该文基于电磁感应原理,设计出了新型线圈发射器模型,该模型利用线圈外部的传感器来控制电路放电时间,克服了传统结构的缺陷。借助SIMPLORER和Maxwell仿真软件对发射器的主体电路和线圈磁场进行分析,得到了磁场中弹丸的静态和动态特性曲线。仿真结果验证了该新型发射器的作用机理,并得到了四级发射器模型的结构参数。该新型发射器可把30g弹丸加速到12.00m/s。结果表明该新型发射器结构简单、线圈内部磁场分布均匀,大大提高了模型的发射效率和系统的稳定性。     

乙酸甲酯催化精馏水解塔过程模拟

分析乙酸甲酯催化精馏水解过程,建立该实验塔物理模型,据此合理简化,建立平衡级与固定床相结合的数学模型及其计算方法.应用该模型方法对本工艺试验的3种催化精馏塔型,分别按2种工况模拟实验过程,得到了与实测数据吻合良好的计算结果.验证了该模型和计算方法的适用性和准确性,能够为过程放大和工艺条件优化提供可靠数据.     

改进符号压力函数的区域活动轮廓模型

利用具有图像增强能力的局部区域信息,定义一种新的符号压力函数(SPF)。用该SPF函数取代GAC模型中的边界停止函数,对GAC模型进行改进,提出一种新的区域活动轮廓模型,从而解决了非同质或弱边界图像的分割问题。继续采用Selective Binary and Gaussian Filtering水平集方法,避免水平集函数的重新初始化,简化新模型。真实图像和合成图像的实验结果表明,新模型与LBF模型具有相同的分割效果,但在计算效率上远优于LBF模型。新模型不仅能够分割非同质或弱边界图像,且具有亚像素分割精确性、抗噪性、局部全局选择分割性等性质。     

Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement

The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA), Monte Carlo simulation and Genetic Algorithm (GA). The proposed model evaluates KM using a set of proxy measures correlated with the major KM processes. Data Collection Budget Allocation (DCBA) that maximizes the model accuracy is determined using GA. Additional data are generated and analyzed using a Monte-Carlo-enhanced DEA model to obtain the overall KM efficiency and KM processes’ efficiency scores. An application of the model has been carried out to evaluate KM performance in higher educational institutions. It is found that with GA, the accuracy of the model has been greatly improved. Lastly, comparing with a conventional deterministic DEA model, the results from the proposed model would be more useful for managers to determine future strategies to improve their KM.     

Memory and expectations in learning,language, and visual understanding

Research in vision and language has traditionally remained separate in part because the classic task of generating a representation of a given image or sentence has resulted in an emphasis on low level structural aspects of these media. In this paper we argue that image and language understanding should be approached with the intent of facilitating the performance of a task. Under this view research in image and language understanding must confront common issues that arise as a task is pursued. Language and images are both input that can be used to maintain a model of a task. We argue that a model may be maintained by incorporating changes in the scene that can be characterized at a high level of abstraction yet manifest themselves at relatively low levels of analysis. Existing task-relevant models and the associated domain knowledge are used to expect specific changes and disambiguate the interpretation of these changes, thereby allowing them to modify the existing model. From this perspective, understanding input is largely independent of the modality of the input.