Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

Constrained Run-to-Run Optimization for Batch Process Based on Support Vector Regression Model

An iterative (run-to-run) optimization method was presented for batch processes under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process.Because support vector machine is powerful for the problems characterized by small samples,nonlinearity, high dimension and local minima, support vector regression models were developed for the end-point optimization of batch processes. Since there is no analytical way to find the optimal trajectory, an iterative method was used to exploit the repetitive nature of batch processes to determine the optimal operating policy. The optimization algorithm is proved convergent. The numerical simulation shows that the method can improve the process performance through iterations.

Supporting Processes of Syndiospecific Metallocene Catalyst for Polymerization of Propylene

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The Process of Support by Nursing Professionals for Families Having a Member with Borderline Personality Disorder

The purpose of this study was to explore the process of family support provided by nurses to families with a borderline personality disorder (BPD) patient. Semi-structured interviews were conducted with 16 nurses who had provided care to BPD patients. Data obtained from the interviews were qualitatively analyzed using a modified grounded theory approach. As an overall core category of family support processes practiced by nurses for families with BPD patients, family support practiced without awareness that the nurses were supporting families was extracted. Through this process, nurses held perceptions that were premises for family support, which were formed through their individual nursing experiences and perspectives. Nurses also had diverse perceptions concerning the image of families. Through the integration of perceptions that were premises for family support and perceptions of an image of the family, nurses underwent a process of “determination and ambivalence about the need for family support.” Then, nurses provided “family support practice” when they acknowledged the need for family support. During the “family support practice,” nurses had difficulties in providing family support. When family support was not successfully provided, nurses provided “family support practice with seeking more effective ways through trial and error.” For cases in which nurses did not acknowledge the need for intervention, they intentionally chose “not to provide family support.” Furthermore, during the “family support practice,” nurses had contradictory perspectives of family support. Such family support processes ultimately led to an awareness of the same family support required for the future. Family support was provided with “family support practice” and “family support practice with seeking more effective ways through trial and error.” In some cases, however, the process ended in “not to provide family support intentionally.” Experiences and perspectives in providing family support are important factors in carrying out future family support. Developing the positive implications of these factors and reducing psychological strain on nurses may ensure smooth implementation of family support. Thus, nurses need to recognize that they are supporting the family, which is identified as a core category.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Determination of reservoir induced earthquake using support vector machine and gaussian process regression

The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /

Enterprise Process Modeling Methodology and Environment Supporting Project Management

Development of the global economy makes modem enterprises confront challenges of efficient managements for large projects and complex processes. Projects are typically managed in rather special manners. On the contrary, there exist many methodologies for product process management to achieve consistency and continuance. However, processes often lack flexibility offered by projects. This paper dis~ the relationship of conceptual characteristics between process and project, gives low-level details to tackle the difference between them, and proposes an enterprise process modeling method for project management. An integrated environment is designed to support the method from which both project management and process management can receive benefits and conform to the limitations.

Development of a Web-Based Decision Support System for Cell Formation Problems Considering Alternative Process Routings and Machine Sequences

In this study, we use the respective advantages of the tabu search (TS) and the Web-based technologies to develop a Web-based decision support system (DSS) for cell formation (CF) problems considering alternative process routings and machine sequences simultaneously. With the assistance of our developed Web-based system, the CF practitioners in the production departments can interact with the systems without knowing the details of algorithms and can get the best machine cells and part families with minimize the total intercellular movement wherever and whenever they may need it. To further verify the feasibility and effectiveness of the system developed, an example taken from the literature is ado- pted for illustrational purpose. Moreover, a set of test problems with various sizes drawn from the literature is used to test the performance of the proposed system. Corresponding results are compared to several well-known algorithms previously published. The results indicate that the proposed system improves the best results found in the literature for 67% of the test problems. These show that the proposed system should thus be useful to both practitioners and researchers.

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase.Support vector machine is first used for phase identification,and for each phase,improved artificial immune network is developed to analyze and recognize fault patterns.A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network.The proposed method has been applied to glutamic acid fermentation,comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

软件过程支持系统EProcessV2.0的研究与实现

软件过程是软件研究中的新兴领域 ,它是提高软件生产率、保证软件质量的重要方法。本文介绍软件过程支持系统 EProcess V2 .0 ,探讨系统中过程建模技术 ,引入 APRML建模语言及其实施机制。同时介绍了 EProcess V2 .0的各个组成部分及其在 J2 EE模式下的具体实现 ,旨在为软件组织提供一种可视化、灵活方便。

Influence of longwall gateroad convergence on the process of mine ventilation network-model tests

Hard coal mines are required to constantly ventilate mine workings to ensure that the air composition is at a certain humidity and temperature level that is comfortable for underground mine workers,especially in deep deposits.All underground workings,which are part of the mine ventilation network,should be ventilated in a way that allows maintaining proper oxygen concentration not lower than 19%(by volume),and limits concentration of gases in the air such as methane,carbon monoxide or carbon dioxide.The air flow in the mine ventilation network may be disturbed due to the natural convergence(deformation)and lead to change in its original cross-section.Reducing the cross-sectional area of the mining excavation causes local resistances in the air flow and changes in aerodynamic potentials,which leads to emergency states in the mine ventilation network.This paper presents the results of numerical simulations of the influence of gateroad convergence on the ventilation process of a selected part of the mine ventilation network.The gateroad convergence was modelled with the finite element software PHASE 2.The influence of changes in the cross-sectional area of the gateroad on the ventilation process was carried out using the computational fluid dynamics software Ansys-Fluent.

Saudi License Plate Recognition Algorithm Based on Support Vector Machine

-License plate recognition （LPR） is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine （SVM） algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

Determination of Quality Properties of Soy Sauce by Support Vector Regression Coupled with SW-NIR Spectroscopy

The modern near-infrared(NIR) spectroscopy analysis is a simple, efficient and nondestructive technique, which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible, and cost-effective method to control product quality in food industry. The method of support vector regression coupled with shortwave NIR spectroscopy was explored for the nondestructive quantitative analysis of the important quality parameters of soy sauce, including amino nitrogen content, total acid content, salt content and color ratio. In this study, the support vector regression(SVR) models based on subtractive spectra and positive spectra were found and compared, the results show that the subtractive spectrum was more excellent than the positive spectrum. Meanwhile, R and RSE were determined, respectively, by means of original spectra and pretreated spectra[standard normal variate (SNV), first-derivative and second-derivative], and the corresponding models were successfully established. The best prediction was achieved by a support vector regression model of the first derivative transformed dataset. In addition, the result obtained by the proposed method was compared with that of Partial Least Squares(PLS), which showed that the generalization performance of the classifier based on SVR was much better than that of PLS. The results demonstrate that shortwave NIR spectroscopy combined with SVR is promising for the quality control of soy sauce.

A Multi-Criteria Decision Support System for the Selection of Low-Cost Green Building Materials and Components

The necessity of having an effective computer-aided decision support system in the housing construction industry is rapidly growing alongside the demand for green buildings and green building products. Identifying and defining financially viable low-cost green building materials and components, just like selecting them, is a crucial exercise in subjectivity. With so many variables to consider, the task of evaluating such products can be complex and discouraging. Moreover, the existing mode for selecting and managing, often very large information associated with their impacts constrains decision-makers to perform a trade-off analysis that does not necessarily guarantee the most environmentally preferable material. This paper introduces the development of a multi-criteria decision support system (DSS) aimed at improving the understanding of the principles of best practices associated with the impacts of low-cost green building materials and components. The DSS presented in this paper is to provide designers with useful and explicit information that will aid informed decision-making in their choice of materials for low-cost green residential housing projects. The prototype MSDSS is developed using macro-in-excel, which is a fairly recent database management technique used for integrating data from multiple, often very large databases and other information sources. This model consists of a database to store different types of low-cost green materials with their corresponding attributes and performance characteristics. The DSS design is illustrated with particular emphasis on the development of the material selection data schema, and application of the Analytical Hierarchy Process (AHP) concept to a material selection problem. Details of the MSDSS model are also discussed including workflow of the data evaluation process. The prototype model has been developed with inputs elicited from domain experts and extensive literature review, and refined with feedback obtained from selected expert builder and developer companies. This paper further demonstrates the application of the prototype MSDSS for selecting the most appropriate low-cost green building material from among a list of several available options, and finally concludes the study with the associated potential benefits of the model to research and practice.

Stress evolution and support mechanism of a bolt anchored in a rock mass with a weak interlayer

By applying experimental method, the bolt stress and supporting mechanism is studied during the deformation process of a rock mass containing a weak interlayer. The force measuring bolt is installed manually and instrumented five pairs of symmetrical strain gauges. The experimental results show that the fully grouted bolt suffers tensile, compressive, bending and shear stress at the same time. The bolt stress evolution is closely related to the deformation stages of the rock mass which are very gradually varying stage, gradually varying stage at the pre-peak and suddenly varying stage at the post peak stage.The axial compressive stress in the bolt is mainly induced by the moment. Thus, in most cases the axial compressive stress is distributed on one side of the bolt. For axial stresses, induced by the axial force and the bending moment at the post-peak stage, three types of changing are observed, viz. increasingincreasing type, decreasing-increasing type and increasing-decreasing type. The stress characteristics of the bolt section in the weak interlayer are significantly different from those in the hard rock. The failure models of the anchored bolt are tensile failure and shear failure, respectively. The bolt not only provides constraints on the free surface of the rock mass, but also resists the axial and lateral loading by the bending moment. This study provides valuable guidelines for bolting support design and its safety assessment.

Processing Time Prediction Method Based on SVR in Semiconductor Manufacturing

The prediction problem of the actual value of the dynamic parameters in the simulation model in semiconductor manufacturing was discussed. Considering the fact that the default value of processing time of one certain equipment in the simulation model was not the same as its actual value,a general data driven prediction model of the processing time was built based on support vector regression( SVR),with the utilization of manufacturing information in manufacturing execution system( MES). The processing time of one certain equipment was highly related to the status of the equipment itself and the wafers being processed. To uncover the relationship of the processing time with the information of historical products,process flow,technical standard of silicon wafers and manual intervention,data were extracted from MES and used to build a prediction model. This model was employed on an ion implantation equipment as a case, and the effectiveness of the proposed method was shown by comparing with other approaches.

Fusion of multi-spectral image and panchromatic image based on support vector regression

In our study, support vector value contourlet transform is constructed by using support vector regression model and directional filter banks. The transform is then used to decompose source images at multi-scale, multi-direction and multi-resolution. After that, the super-resolved multi-spectral image is reconstructed by utilizing the strong learning ability of support vector regression and the correlation between multi-spectral image and panchromatic image. Finally, the super-resolved multi- spectral image and the panchromatic image are fused based on regions at different levels. Our experi- ments show that, the learning method based on support vector regression can improve the effect of super-resolution of multi-spectral image. The fused image preserves both high space resolution and spectrum information of multi-spectral image.

A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An AI-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.

Exploring the Characteristics of Business Process Modeling Solutions in the Saudi Market

Business Process Modeling (BPM) is a mechanism that separates all business aspects from the underlying technological and implementation features of a system. The aim is to capture an organization’s processes and achieve its business objectives. Currently, there are many solutions for Business Process Modeling and Design offered by vendors. However, the selection of one solution or another by customers is usually conducted in an ad-hoc manner. Given the underlying environment that a customer might have and their limitations, there is no standard methodology that can help in the selection of the most appropriate solution. This paper therefore highlights the key characteristics of BPM solutions in the market to facilitate an understanding of the compatibility of a given solution with customer’s environments;hence, customers can then make informed decisions regarding their selections.