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Intelligent Engineering Systems (INES), 2012 IEEE 16th International Conference on

Date 13-15 June 2012

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Displaying Results 1 - 25 of 105
  • Committees

    Page(s): 12 - 13
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  • [Copyright notice]

    Page(s): 2
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  • General information

    Page(s): 10
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  • Table of contents

    Page(s): 3 - 9
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  • [Title page]

    Page(s): 1
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  • Welcome message from the general chairs

    Page(s): 11
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  • Intelligent system for optimization of production in flexible manufacturing lines

    Page(s): 217 - 222
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (729 KB) |  | HTML iconHTML  

    Intelligent methods are used in modeling and control of production systems that belong to different industrial areas. This paper proposes an intelligent system for a more efficient production in flexible manufacturing lines. The system can be used for minimizing production time of different components, in process production. The main characteristic of this intelligent system is to be dynamic and this means to respond to changes that occur in the working environment which it belongs to. The presented system is suitable for implementation on various types of flexible manufacturing in order the production to be optimized. View full abstract»

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  • DBSCAN-GM: An improved clustering method based on Gaussian Means and DBSCAN techniques

    Page(s): 573 - 578
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (617 KB) |  | HTML iconHTML  

    Clustering is one of the most useful methods of intelligent engineering domain, in which a set of similar objects are categorized into clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. This paper presents an efficient and effective clustering technique, named DBSCAN-GM that combines Gaussian-Means and DBSCAN algorithms. The idea of DBSCAN-GM is to cover the limitations of DBSCAN, by exploring the benefits of Gaussian-Means: it runs Gaussian-Means to generate small clusters with determined cluster centers, in purpose to estimate the values of DBSAN's parameters. The results of our method show that it is efficient even for large data sets especially data with large dimension and capable to handle noises, contrary to partitioning algorithms such as K-Means or Gaussian-Means. Additionally, DBSCAN-GM does not necessitate any priori information, in contrast to the density clustering DBSCAN obliging two input parameters which are hard to guess, namely Eps (the radius that bounds the neighborhood region of an object) and MinPts (the minimum number of objects that must exist in the objects neighborhood region). Simulative experiments are carried out on a variety of datasets, which highlight the DBSCAN-GM's effectiveness and cluster validity to check the good quality of clustering results. View full abstract»

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  • Improvement of the measurement update step of EKF-SLAM

    Page(s): 61 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    In this study, the measurement update step of the Extended Kalman Filter (EKF)-based Simultaneous Localization and Mapping (SLAM) is improved. The computational complexity of the measurement uncertainty matrix inversion operation in the measurement update step is reduced via using Jacobi iteration method. It is observed that, the calculation of the measurement uncertainty matrix inverse by using Jacobi iteration method generates numerically more stable results than naive single and batch update operations. Moreover, it produces more accurate results than the results of Cholesky decomposition with less complexity. View full abstract»

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  • Fast robot voice interface through Optimum-Path Forest

    Page(s): 67 - 71
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (345 KB) |  | HTML iconHTML  

    Voice-based user interfaces have been actively pursued aiming to help individuals with motor impairments, providing natural interfaces to communicate with machines. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for voice-based robot interface, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster. Experiments were conducted against Support Vector Machines, Neural Networks and a Bayesian classifier to show the OPF robustness. The proposed architecture provides high accuracy rates allied with low computational times. View full abstract»

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  • Special materials used in FDM rapid prototyping technology application

    Page(s): 73 - 76
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (831 KB) |  | HTML iconHTML  

    In this paper are presented information about common and advanced materials used for manufacturing of products by Fused Deposition Modelling (FDM) rapid prototyping technology. In different rapid prototyping technologies the initial state of material can come in either solid, liquid or powder state. In solid state it can come in various forms such as pellets, wire or laminates. The current range materials include paper, nylon, wax, resins, metals and ceramics. In FDM are mainly used as basic materials ABS - Acrylonitrile Butadiene Styrene, polyamide, polycarbonate, polyethylene and polypropylene. For special FDM applications are used advanced materials as silicon nitrate, PZT, aluminium oxide, hydroxypatite and stainless steel. View full abstract»

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  • Production processes with integration of CA data in augmented reality environment

    Page(s): 77 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1895 KB) |  | HTML iconHTML  

    This article describes possibilities of assembling process realized with using of special virtual elements of the augmented reality (AR) and production processes with integration of parametrical CA data in virtual environment by graphical script. These possibilities are implemented in the virtual assembling environment of AR, where the application core allows engineers and constructors not only to see important information about a position and orientation of the single assembly element but also view on next item from assembly list. By means of this the costumer can see the motion process of single assembly item according to its trajectory and necessary information about general characteristic of the assembling processes. View full abstract»

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  • Vibration diagnostic test for effect of unbalance

    Page(s): 81 - 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3170 KB) |  | HTML iconHTML  

    The significance of the preventive maintenance is increasing in every area of the life, for example education and production industry. When we are using a rotating machine or a mechanical structure there are many forces and torques which load it, and create mechanical vibrations. This paper presents a very useful method of the preventive maintenance vibration diagnostic with test of moving-animation. We can see and understand the moving of the machines and the mechanical structures by the moving-animation software and a vibration analyzer instrument when they vibrate. The result of this method helps to make faster and more correct diagnosis of the damage. This article shows the basic knowledge of the vibration diagnostic with animation program, and two case studies from my practice. The case studies show the moving of the machines in case of mechanical problems. Through the case studies we can see two significant applications. The first one is an example of three unbalanced discs on a test bench, and the second one is a special unbalance problem of a rotating machine in the industry. View full abstract»

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  • Twin diagnostic method for industrial robot maintenance

    Page(s): 87 - 89
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1030 KB) |  | HTML iconHTML  

    Efficient system diagnostics has paramount significance in avoiding financial losses due to unexpected outages that may be caused by failures and/or damages. Proper maintenance strategies therefore need reliable technologies for observing erroneous operation and forecasting the potential consequences. With the exception of deliberate applications as braking etc. energy dissipation is undesired phenomenon in the operation of mechatronic devices. As a consequence, they are designed with the intent of minimizing dissipation as much as possible. The presence of intensive dissipation i.e. warming of various components normally conveys information on erroneous operation that may be caused by either mechanical or electrical components, or both. Combined application of thermometry and vibration analysis can effectively reveal the actual problem behind the symptoms and provides the users with good forecast. This method can be realized with relatively simple measuring apparatus. In the paper the details of this methodology are presented. View full abstract»

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  • Hausdorff-distance enhanced matching of Scale Invariant Feature Transform descriptors in context of image querying

    Page(s): 91 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1710 KB) |  | HTML iconHTML  

    Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed. View full abstract»

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  • Comparison of several classification algorithms for gender recognition from face images

    Page(s): 97 - 101
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    This paper presents a comparison between several algorithms which were employed for gender recognition automatically. Firstly, the face images of various mature women and men samples were gathered, and face images were separated as train dataset and test dataset. Both of the datasets were pre-processed and made ready for following operations. Secondly, Principal Component Analysis (PCA) was applied to train dataset to extract the most distinguishing features. Finally, three classification algorithms, Support Vector Machine (SVM), k-Nearest Neighbourhood (k-NN), and Multivariate Classification with Multivariate Gauss Distribution (MCMGD) algorithms were implemented and compared to determine the most suitable and successful algorithm for gender recognition from face images. Experimental results illustrate that k-NN with k values 5, 7, 9 outperformed the other approaches. View full abstract»

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  • Detection of the mite Varroa destructor in honey bee cells by video sequence processing

    Page(s): 103 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (693 KB) |  | HTML iconHTML  

    It is presented an algorithm for detecting and tracking a mite, from videos provided by the Centro de Investigación Apícola Tropical (CINAT-Costa Rica). These registrations correspond to the presence of the Varroa destructor mite in an Africanized honey bee cell, in a controlled environment. The main objective in this paper is to present the various stages of development of the algorithm and show the results obtained in the percentage of success in detecting. Therefore, we have implemented a calibration system in order to have a frame more enhanced versus the original video. This calibration is done by the searching of the Movement Active Area and definition of our object (the mite V destructor), and finally, an automatic detection and tracking are done. We have reached up to 93.75% for right detection and tracking, working on real time under a Matlab environment. View full abstract»

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  • Knowledge-based zooming technique for the interactive visual analysis of mechatronic systems

    Page(s): 109 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5108 KB) |  | HTML iconHTML  

    The visual analysis of mechatronic systems requires a large set of visualization techniques in order to analyze the different kinds of generated data. When the analysis is carried out online, the user needs the aid of automatic techniques that shift his/her attention to important incidents, e.g. an occurred error. In this paper we present a knowledge-based zooming technique for virtual environments. This technique incorporates a level-of-detail technique and a knowledge-based visualization selection. The user can interactively change the level of information, thereby, a knowledge-based technique selects the significant incidents and notifies the user. Our application demonstrates the feasibility of this technique. View full abstract»

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  • Software agents: Can we trust them?

    Page(s): 15 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (429 KB) |  | HTML iconHTML  

    In this paper we briefly describe multi-agent system architectures that are proved to be suited to address problems which simultaneously are of a Distributed, Decentralized and Dynamic nature. Two clearly different scenarios are used to show how the multi-agent system paradigm can be used to cope with realistic situations which are eiter mainly cooperative or mainly competitive and, thus, asking for specific characteristics that make those systems trustworthy. Negotiation protocols are used for the sake of getting, through cooperative strategies involving several agents, commonly accepted final solutions for an overall goal. We also advocate the use of Trust measures, together with normative environments that enforce legality, to make multi-agent systems useful in real demanding scenarios, like it is the case in competitive B2B situations. View full abstract»

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  • On extended Similarity Scoring and Bit-vector Algorithms for design smell detection

    Page(s): 115 - 120
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (767 KB) |  | HTML iconHTML  

    The occurrence of design smells or anti-patterns in software models complicate development process and reduce the software quality. The contribution proposes an extension to Similarity Scoring Algorithm and Bit-vector Algorithm, originally used for design patterns detection. This paper summarizes both original approaches, important differences between design patterns and anti-patterns structures, modifications and extensions of algorithms and their application to detect selected design smells. View full abstract»

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  • Test calculation for logic and short-circuit faults in digital circuits

    Page(s): 121 - 124
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB) |  | HTML iconHTML  

    In the first part, the paper presents a test calculation principle which serves for producing tests of logic faults in digital circuits. The name of the principle is composite justification. The considered fault model includes stuck-at-0/1 logic faults. Both single and multiple faults are included. In this paper only combinational logic is taken into consideration. The computations are performed at the gate level. In the second part of the paper, the composite justification is extended to an other fault class, namely, short-circuit faults. A short circuit is an erroneous galvanic coupling between two circuit lines. The calculation principle is comparatively simple. It is based only on successive line-value justification, and it yields an opportunity to be realized by an efficient computer program. View full abstract»

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  • Neural network based identification of hand movements using biomedical signals

    Page(s): 125 - 129
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (334 KB) |  | HTML iconHTML  

    This paper proposes a methodology that analysis and classifies the EMG and MMG signals using a linear neural network to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG and MMG signals classification system was established using a linear neural network and it is presented the comparison with the classification based on the LVQ neural network. Experimental results show a promising performance in classification of motions based on both MMG and EMG signals. View full abstract»

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  • Distributed intrusion detection system using self organizing map

    Page(s): 131 - 134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (562 KB) |  | HTML iconHTML  

    The goal of the article of to present intrusion detections system and design a two layer architecture of distributed intrusion detection based on the neural network self-organizing map. First layer consists of detection sensors which provide basic processing of input data on behalf of statistical methods with a direct connection to countermeasure modules. Performance and accuracy of the modeling system is ensured by using central distributed processing, in which the detection of generalized description with self-organizing map is used, preventing the intrusion itself. View full abstract»

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  • Field Programmable Gate Array implementation of Conic Section Function Neural Network: An alternative to analog CSFNN circuitry

    Page(s): 135 - 138
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB) |  | HTML iconHTML  

    In this study, Field Programmable Gate Array (FPGA) implementation of Conic Section Function Neural Network (CSFNN) for a classification problem focused on iris plant is presented. This work demonstrates for the first time to our knowledge, the feed-forward computation of CSFNN implementation on FPGA. Using 16-bit floating point arithmetic and the look-up tables (LUTs) for the sigmoid function and the square root function, 83% and 72% of slices and LUTs on Spartan 3-E XC3S1600E are used for the realization of CSFNN with five neurons. The classification results obtained from the FPGA implementation and software simulation show that the accuracy error between two platforms is only 0.1%. View full abstract»

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  • Neighbors cooperation in WSN based on collective decisions

    Page(s): 139 - 143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    The paper describes the concept of neighborhood cooperation in context of the Wireless Sensor Network, in which each node can identify and communicate with neighbors around it. The innovative spatial routing scheme shapes an area where the packet retransmission toward the base station can take effect. In this work, the retransmission rules that use notion of actions limited to node's neighborhood are determined. The proposed solution based on collective cooperation within neighborhood to support routing decisions was implemented and tested in Matlab environment. The simulation results show an improvement in the network adaptability to disturbances and as well as to changes of propagation conditions in the environment. View full abstract»

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