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Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Date 11-12 July 2009

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Displaying Results 1 - 25 of 179
  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - xv
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  • Message from Conference Chairs

    Page(s): xvi
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  • Organizing Committee

    Page(s): xvii
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  • Program Committee

    Page(s): xviii
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  • list-reviewer

    Page(s): xix
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  • Research on Prognostic and Health Monitoring System for Large Complex Equipment

    Page(s): 3 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (298 KB) |  | HTML iconHTML  

    Prognostic and health monitoring (PHM) is the latest development of large complex equipment management. An extensible distributed prognostic and health monitoring system based on hybrid wireless network techniques was studied and designed in this paper. Firstly,after a careful analysis on the structure and tasks of the equipment, the general PHM framework of large complex equipment is proposed. Then the technical program and network of system are presented. The corresponding on-line monitoring, diagnosis and management system are designed.And the fault prediction technologies are discussed. Finally,the health management and decision system is studied. Anew technical plan that skillfully combines knowledge base,model base, database, their management systems and inference engine is proposed. And the realization is introduced. The results of practical operation show that,because of the using of hybrid wireless network, embedded system, prognostic and intelligent diagnosis, the system maybe flexibly deployed, have powerful capability of monitoring,diagnosis and management, and can implement expeditious and accurate prognostic and fault diagnosis for large complex equipment system. View full abstract»

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  • Development and Implementation of Software Gateways of Fire Fighting Subsystem Running on EBI

    Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (237 KB) |  | HTML iconHTML  

    This paper explains in detail two methods of developing software gateways of fire fighting subsystem running on based on an integration project of intelligent building system completed by the author recently. One method is to use the Agent which is developed based on AI Agent theory and runs between EBI and MK7022. This Agent can be used as the software gateways of fire fighting subsystem and integrate the fire fighting subsystem into BMS ldquomodule parallel integration modelrdquo. Another method is to develop a driving COM component of fire fighting subsystem which is called by OPC Server to realize the system integration based on OPC component integration.. View full abstract»

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  • The Optimal Application of the Algorithms of Detection and Data Mining in Honeynet

    Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (262 KB) |  | HTML iconHTML  

    This paper puts forward a technical scheme which properly arranges IDS and optimally applies the algorithms of detection and data mining to the Honeynet environment based on a project of building automation system completed by the author recently. In this specific environment, the position of IDS is deployed reasonably and the anomaly and misuse detection algorithm of IDS is designed and selected optimally. Meanwhile, the misuse detection rules are updated dynamically with the combination of data-mining algorithm RIPPER. The design makes the classical and mature algorithms of anomaly detection, misuse detection and RIPPER data mining display their technical characteristics and advantages to the largest extent in the project and enable the Honeynet to protect the internal control network as expected. View full abstract»

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  • Incremental Updating Algorithm Based on Partial Support Tree for Mining Association Rules

    Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (262 KB) |  | HTML iconHTML  

    A new algorithm, which is based on partial support tree (PS_Tree), is proposed to deal with the incremental updating problem when a new database is inserted and the minimum support is not changed. This algorithm use effectively the association rules mined and the partial support tree reserved to improve the performance. It only need scan the updated part of the database once so that the efficiency of algorithm can be further improved . The performance study shows that the algorithm is efficient for incremental updating problems of association rules. View full abstract»

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  • The Application of Membership Degree Transformation New Algorithm in Military Transportation Performance Evaluation

    Page(s): 21 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (315 KB) |  | HTML iconHTML  

    The core of performance fuzzy evaluation is membership degree transformation. But the transformation methods should be questioned, because redundant data in index membership degree is also used to compute object membership degree, which is not useful for object classification. The new algorithm is: using data mining technology based on entropy to mine knowledge information about object classification hidden in every index, affirm the relationship of object classification and index membership, eliminate the redundant data in index membership for object classification by defining distinguishable weight and extract valid values to compute object membership. The paper applied the new algorithm in the fuzzy evaluation on military transportation performance. View full abstract»

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  • Four Statistical Approaches for Multisensor Data Fusion under Non-Gaussian Noise

    Page(s): 27 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (255 KB) |  | HTML iconHTML  

    Multisensor data fusion methods for Gaussian noise are widely reported, while fusion approaches for non-Gaussian noise are seldom met in the literature. In this study, four statistical fusion methods are presented for a mixture of Gaussians noise. These four methods are the minimum variance approach, the maximum kurtosis approach, the minimum kurtosis approach, and the minimum mean absolute error approach. Preliminary numerical simulations demonstrate that the maximum kurtosis method shows the worst fusion performance, while the rest three methods shows equivalent better fusion performance. View full abstract»

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  • A Direct-identifying GPC Implicit Algorithm Based on Two Identifiers

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB) |  | HTML iconHTML  

    Based on characteristics of district heating system, a new GPC (generalized predictive control) algorithm was presented that directly identified controllerpsilas parameters with two identifiers. The new method could adapt the system of great inertia, time-varying parameters and rather large delay. In addition, it could omit computation of inverse matrix and Diophantine equations, and thus could greatly reduce operation process and operation time. Finally, by the simulation research of heat-exchanging process, the influence of feedforward compensation is analyzed, and the validity of the new algorithm is proved. View full abstract»

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  • Adaptive Multiple-Model Control of a Class of Nonlinear System Using Soft Computing

    Page(s): 35 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (261 KB) |  | HTML iconHTML  

    In this note, an adaptive multiple-model controller was developed for a class of nonlinear systems. The multiple models technique was used to describe the most appropriate model at different environments. By designing a blending instead of switching scheme, some models close to the real plant can be selected quickly, so that the transient performance can be improved significantly. Unlike previous results, we do not require a switching scheme to guarantee the most appropriate model to be chosen which can simplify the analysis of the stability of the closed-loop system. Besides, the proposed adaptive controller is a continuous type controller. View full abstract»

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  • The Using of Knowledge Visualization Tools in E-science Environment Take Concept Maps for Example

    Page(s): 39 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB) |  | HTML iconHTML  

    In e-science environment, people pay more attention to the scientific visualization and data visualization. Visualization about information exchange and knowledge sharing is rare to be mentioned. Knowledge sharing is an important part in e-science environment. The knowledge visualization tools can facilitate thinking together and the collective wisdom, it also can promote the knowledge sharing and the creation of the new knowledge. If the concept maps, a visualization tool, are used to help members in e-science environment to describe their structure of knowledge in communication, it can promote the knowledge discovery in a clearer way. In this paper, we discuss the application of concept maps and we promote an application model in e-science environment. View full abstract»

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  • Prediction of Reservior Runoff Using RBF Neural Network-Grey System United Model

    Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (283 KB) |  | HTML iconHTML  

    At present, classic methods are used to predict reservoir runoff, but the result is not ideal. Due to the shortages of neural network and grey system, in this paper, a grey neural network model is set up based on grey and neural network theory. The data got from the GM(1, 4) on the factors affecting the reservoir runoff is used as the input of the neural network and the origin data of reservoir runoff are used as the output of neural network which was trained to get the optimal structure of neural network. The results show that the model had highly fitting and predicting precision advantages than other model had. The case study shows that the model is quite accurate in prediction reservoir runoff, which has some project referential value. View full abstract»

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  • PSO-Based RBF Neural Network Model for Teaching Quality Evaluation

    Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    In view of the problems existing in previous system of teaching quality, according to our teaching characteristics a new model of PSO-based teaching quality evaluation is set up by means of PSO theory and neural network. And the application procedure of the model is illuminated in detail. By analyzing a lot of practical examples, the experiment result indicates that this mathematical model has better appraisal effect and can overcome the complexity of traditional evaluation model. Compared with other methods, this method is scientific, simple and operable. And its structure and method will have a bright future. View full abstract»

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  • A Study on Multivariate Forecast Model of Threshold Gradient Based on Grey System Theory

    Page(s): 51 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (238 KB) |  | HTML iconHTML  

    It is well known that there is threshold pressure gradient in low permeability porous media. The mathematic principle of GM(1,N) for threshold pressure gradient was firstly studied. Then the actual measurement data of threshold pressure gradient during the experiment were collected, and 11 reliable data were gained. By selecting 4 factors including viscosity, permeability, density and porosity as the grey modeling forecast indicators, the multivariable forecast model of GM(1,4) for threshold pressure gradient were respectively constructed. The result calculated by use of the model shows that the precision meets the requirements of engineering and the model can be used for predicting TPG. The application of this approach can supply basic data for the development of low permeability oilfield so as to save cost and labor. View full abstract»

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  • A Hybrid Differential Evolution Method for Practical Engineering Problems

    Page(s): 54 - 57
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB) |  | HTML iconHTML  

    This paper introduced an almost control parameters free modified differential evolution for global optimization problems. The modifications are derived from the mechanisms of particle swarm optimization viz., topologies, inertial weight, neighborhood best and individual best, with which each individual performed the mutation operator based on its current position, the neighborhood best and its individual best along with the inertial weight. And the crossover operator in traditional differential evolution is removed, while the selection operator was employed. The approach was employed for a tension/compression string design problem and an economic dispatch problem in power system. By comparisons with the other evolutionary algorithms, the proposed approach has shown its feasibility and effectiveness. View full abstract»

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  • A Decision Support System for Supply Chain Management Based on PSO and GIS

    Page(s): 58 - 61
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (611 KB) |  | HTML iconHTML  

    A decision support system to solve the multi-depot vehicle routing problems with fixed distribution of vehicles (MDVRPFD) is introduced. In the system, the data of the customers, the suppliers, and the topologies of the roads are stored and managed by the geographic information system (GIS), and a modified genetic particle swarm optimization method (MGPSO) is employed to provide the routings of the vehicles. In the MPSO, the two-stage approach is employed which decomposes the MDVRPFD into two independent sub problems, viz., assignment and routing, and solves them separately. The system has been implemented to a practical supply chain management system and provided decision supports for both vehicles routing and decision-making on depots distributing. The simulation results have shown the feasibility and effectiveness of the system. View full abstract»

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  • Resource Planning and Scheduling of Payload for Satellite with a Discrete Binary Version of Differential Evolution

    Page(s): 62 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (267 KB) |  | HTML iconHTML  

    The resource planning and scheduling technology of payload is to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. Based on the analysis of the satellitepsilas functions and the payloadpsilas resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is employed along with dividing the planning and scheduling period to several pieces. Consequently, the planning and scheduling is modeled as a combinatorial optimization. To solve the resource planning and scheduling, a discrete binary version of differential evolution (DBDE) is employed which was derived from the traditional differential evolution (DE) and customized to combinatorial optimizations. The simulation results have shown the feasibility and effectiveness. View full abstract»

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  • Comprehensive Gene Ontology Mapping Strategies for Improved Biological Inference

    Page(s): 66 - 69
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (281 KB) |  | HTML iconHTML  

    Lack of sufficient semantic relationships between pairs of terms coming from the three independent gene ontology sub-ontologies, that limit the power to provide complex semantic queries and inference services based on it. By integrating non-lexical and lexical learning strategies into GLUE system, we semi-automatically generate six types of one-to-one mapping paths covered almost half of all GO terms. We believe that the comprehensive ontology mapping strategies might be an effective way to bridge the gap between non-lexical and lexical approaches, as well as improve accuracy and coverage. View full abstract»

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