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Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011 IEEE International Conference on

Date 19-21 Sept. 2011

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Displaying Results 1 - 25 of 34
  • [Front cover]

    Publication Year: 2011 , Page(s): c1
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  • [Copyright notice]

    Publication Year: 2011 , Page(s): ii
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  • Table of contents

    Publication Year: 2011 , Page(s): iii - viii
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  • Welcome message from the chairpersons

    Publication Year: 2011 , Page(s): ix - xi
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  • An ANN based hyperspectral waterway control and security system

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1449 KB) |  | HTML iconHTML  

    In this paper we report on some of the current advances in the development of Hywacoss (Hyperspectral waterway control and security system). The objective of the Hywacoss project is to produce a real time small, light and easy to transport visible and near infrared hyperspectral detection and recognition system that autonomously monitors waterways, especially port and bay areas, and detects and classifies all the traffic, producing alerts when previously unknown objects or behavior patterns arise. Obviously, Hywacoss involves dedicated hardware and software modules, some of them based on computational intelligence methods. Here we will provide a global description of the system and a detailed analysis of some of its modules, in particular those related to hyperspectral image segmentation and the Artificial Neural Network based spectral-geometrical identification and profiling subsystems. View full abstract»

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  • Facial expression anlysis using eye gaze information

    Publication Year: 2011 , Page(s): 1 - 4
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (538 KB) |  | HTML iconHTML  

    Psychologists found that human eye gaze direction has a very strong influence on facial expression. However, there exist few researches that took eye gaze direction into consideration while recognizing facial expressions. This paper makes use of the combination of eye gaze and facial expression information to detect human emotion. First, eye gaze direction is categorized into direct gaze and avert gaze. We then carry on facial expression recognition based on the eye gaze analysis results. This could provide the confusion between some of the facial expressions therefore improve the recognition rate of emotion detection. Experimental results show that our proposed method can provide more accurate recognition rate then recognizing facial expression alone. View full abstract»

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  • Hand gesture detection and recognition using principal component analysis

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1212 KB) |  | HTML iconHTML  

    This paper presents a real time system, which includes detecting and tracking bare hand in cluttered background using skin detection and hand postures contours comparison algorithm after face subtraction, and recognizing hand gestures using Principle Components Analysis (PCA). In the training stage, a set of hand postures images with different scales, rotation and lighting conditions are trained. Then, the most eigenvectors of training images are determined, and the training weights are calculated by projecting each training image onto the most eigenvectors. In the testing stage, for every frame captured from a webcam, the hand gesture is detected using our algorithm, then the small image that contains the detected hand gesture is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined between the test weights and the training weights of each training image to recognize the hand gesture. View full abstract»

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  • Intelligent control of bioreactor landfills

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1004 KB) |  | HTML iconHTML  

    One booming concept that has recently gained significant attention in waste management is the “bioreactor landfill”. Despite the potential benefits of operating landfills as bioreactors, there are no standardized operational guidelines and procedures for the system due to the numerous processes and site-specific variables involved. This paper introduces an innovative technology that employs automated monitoring and expert control in the operation of bioreactor landfills. The proposed control system combines multiple interacting hardware and software components, and is coined as SMART (Sensor-based Monitoring and Remote-control Technology). SMART features a fuzzy logic decision engine that mimics the control actions of an experienced human operator. This technology aims to provide optimum conditions for the biodegradation of municipal solid waste, and also, to improve the profitability of the bioreactor landfill in terms of biogas production and space recovery. View full abstract»

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  • Application of fuzzy logic in modern landfills

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1162 KB) |  | HTML iconHTML  

    Landfill is by far the dominant and most economical method for the disposal of solid waste worldwide. The landfill ecosystem involves several physical, chemical, and biological processes that take place simultaneously. The complexity of the landfill processes as well as the uncertainty of solid waste characteristics have led to the implementation of unconventional techniques in modeling the system. In fact, no conventional model could be successfully developed for such a nonlinear ill-defined system because it is practically impossible to isolate the individual effect of its variables and satisfactorily identify its behaviour. Recently, knowledge-based techniques, such as fuzzy logic, became widely used to model complex systems based on qualitative knowledge about their behaviour. This paper presents an implementation of fuzzy logic to solve a serious operational problem in modern landfills. A typical sanitary landfill evolves through consecutive operational phases which are hard to distinguish and characterize. The identification of these phases is vital because each phase has different requirements that have to be met in order to assure safe and smooth transition from one phase to another. A fuzzy logic controller was developed to identify the operational phase of a landfill at a given time based on certain quantitative characteristics of the leachate generated and biogas produced. View full abstract»

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  • On stockpile planning using a multi-objective genetic algorithm

    Publication Year: 2011 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1682 KB) |  | HTML iconHTML  

    The North Atlantic Treaty Organization (NATO) Stockpile Planning Committee (SPC) periodically determines if NATO member nations have the necessary munitions for a full range of mission types, accomplished through the use of a model that minimizes the cost of the required stockpile. We were tasked to examine how the methodology of this model could be modified to allow individual nations to better determine their requirements for Precision-Guided Munitions (PGMs). The approach we undertook involves augmenting the methodology of the model with a multi-objective optimization approach using a genetic algorithm, in which the solution is optimized along two competing objectives: total cost (which is minimized), and the usage of PGMs (which is maximized). We recommended that the SPC consider including this change in all future versions of ACROSS. View full abstract»

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  • Wildfire smoke detection using computational intelligence techniques

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1284 KB) |  | HTML iconHTML  

    In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions. View full abstract»

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  • An adaptable system for energy management in intelligent buildings

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (807 KB) |  | HTML iconHTML  

    In this paper, an adaptable system model for energy management in intelligent buildings is investigated. The application of wireless sensor networks and adaptive learning techniques, in order to bring forward an “Adaptable Systemic Solution” is described. Furthermore, conceptual model and high level architecture of an adaptable system for energy management in “Intelligent Buildings” is proposed. The importance of an adaptable system encompassing few subsystems, sharing knowledge and data is described. The analytical model of a novel Adaptive Learning System capable to learn and adapt by exploiting a rules-based expert system and adaptive learning principles, is proposed and described. Its use for an enhanced version of a Programmable Communicating Thermostat is discussed. View full abstract»

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  • Personal dosimeter for the measurement of artificial optical radiation (AOR) exposure

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1003 KB) |  | HTML iconHTML  

    The Italian Legislative Decree dated April 26, 2010 no. 81/08 also refers to the European directive no. 2006/25/EC on the limit values for the exposure of workers to artificial optical radiation (AOR). The main damages caused by higher exposure to AOR regard in particular eyes and all of the body (e.g. skin). Recent studies concern the health effects on retinal photoreceptors after exposure to wavelength range between 380 nm and 500 nm, named "blue light”. The aim of this paper is to present an innovative personal dosimeter for AOR detection. The proposed system can be used not only for evaluating AOR, but also providing the operator's position and attitude in relation to the natural or artificial source of radiation. The acquired Data are then processed by a Fuzzy Inference System (FIS). The FIS main target is the accurate evaluation of risk levels associated to each light radiation striking the operator's retina. View full abstract»

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  • A Gaussian radial basis function based feature selection algorithm

    Publication Year: 2011 , Page(s): 1 - 4
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    Recently Li et al. proposed a parameter selection method for Gaussian radial basis function (GRBF) in support vector machine (SVM). In his paper cosine similarity was calculated between two vectors based on the properties of GRBF kernel function. Li's method can determine an optimal sigma in SVM and thus efficiently improve its performance, yet it is limited by only focusing on a fixed original feature space and may suffer if the space contains some irrelevant and redundant features, especially in a high-dimensional feature space. In this paper, Li's method is extended to a flexible feature space so that feature selection and parameter selection are conducted at the same time. A feature subset and sigma are determined by minimizing the objective function that considers both within-class and between-class cosine similarities. Our experimental results demonstrate that the proposed method has a better performance than Li's method and traditional SVM in terms of classification accuracy. View full abstract»

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  • Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in Benign Pro Static Hyperplasia cases (BPH)

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (717 KB) |  | HTML iconHTML  

    Predicting the clinical outcome prior to minimally invasive treatments for Benign Prostatic Hperlasia (BPH) cases would be very useful. However, clinical prediction has not been reliable in spite of multiple assessment parameters, such as symptom indices and flow rates. In our prior study, Artificial Intelligence (AI) algorithms were used to train computers to predict the surgical outcome in BPH patients treated by TURP or VLAP. Our aim was to investigate whether, based on eleven clinical biomarker features, AI can reproduce the clinical outcome of known cases and assist the urologist in predicting surgical outcomes. In this paper, the objective is to perform data analysis to investigate if specific features have a greater impact on predicting whether the patients had the desired outcome after a surgical procedure is done. Finally, how the number of significant features ought to be weighted to predict the outcome after surgery, is determined to create the most accurate prediction method. Here both the Decision Tree and Naïve Bayse machine learning methods are used and compared. View full abstract»

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  • Salient features based on visual attention for multi-view vehicle classification

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1215 KB) |  | HTML iconHTML  

    The continuous rise in the amount of vehicles in circulation brings an increasing need for automatically and efficiently recognizing vehicle categories for multiple applications such as optimizing available parking spaces, balancing ferry load, planning infrastructure and managing traffic, or servicing vehicles. This paper describes the design and implementation of a vehicle classification system using a set of images collected from 6 views. The proposed computational system combines human visual attention mechanisms to identify a set of salient discriminative features and a series of binary support vector machines to achieve fast automated classification. An average classification rate of 96% is achieved for 3 vehicle categories. An improvement to 99.13% is achieved by using additional measurement on the width and height of the vehicles. View full abstract»

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  • Ship roll motion time series forecasting using neural networks

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4609 KB) |  | HTML iconHTML  

    A neural network based system has been applied for forecasting the large amplitude roll motions of a ship that appear during parametric roll resonance. Under these conditions, ship roll motion presents a highly nonlinear behavior and accurate predictions are difficult to achieve using classical mathematical modeling approaches. The results obtained present very good agreement to reality, leading to the possibility of applying the system as a base for a parametric roll warning system. View full abstract»

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  • Neural fractal prediction of three dimensional surface roughness

    Publication Year: 2011 , Page(s): 1 - 4
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1921 KB) |  | HTML iconHTML  

    This paper presents a methodology for using the high resolution three dimensional (3D) surface data of fabric samples to acquire their surface roughness parameter measurement. Firstly, we compute a parameter FDFFT, which is the fractal dimension estimated from the two-dimensional fast Fourier transform (2DFFT) of 3D surface scan. We validate the rotation-invariance and scale-invariance of FDFFT using fractal Brownian images. Secondly, in order to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from 3D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3D surface data. Finally, we attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance to both training samples and test samples. View full abstract»

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  • Designing a fuzzy expert system to predict the concrete mix design

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (833 KB) |  | HTML iconHTML  

    The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental results show that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%. View full abstract»

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  • Classification of gear damage levels in planetary gearboxes

    Publication Year: 2011 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB) |  | HTML iconHTML  

    Linear discriminant analysis (LDA) is a method of feature extraction that has demonstrated successful applications. The selection of the number of discriminant directions (r) is important to LDA, yet little attention is paid in the reported literature. In this paper a method is proposed for determining the optimal r in terms of the classification accuracy of support vector machine. The method is applied to identify gear damage levels in a planetary gearbox. Planet gears with four damage levels labeled as baseline, slight, moderate, and severe were used in lab experiments for data collection. Results demonstrate that the proposed method outperforms two reported methods and is effective to address the given problem. View full abstract»

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  • Classification of road conditions: From camera images and weather data

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (825 KB) |  | HTML iconHTML  

    It is important to correctly determine road condition as it contains essential information for improving traffic safety. Knowledge about the road condition is used by maintenance personnel as a trigger for snow removal and deicing tasks. The presence of severe road conditions is also communicated as warnings and speed reduction recommendations to road users. Previous research shows that road images and data from Road Weather information Systems (RWiS) give enough information to identify road conditions, such as dry, wet, snowy, icy and tracks. The hypothesis of the new model was that it should be possible to develop a model that could classify road conditions from existing RWiS road weather data and road images. This paper proposes a model that gives a correct classification of the road conditions dry, wet, snowy and icy at an accuracy rate of 91% to 100%. View full abstract»

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  • Discriminating gaseous emission patterns in low-cost sensor setups

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB) |  | HTML iconHTML  

    This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human-understandable inference rules describing the kind of emission measured. The technique, challenged by the high cross-sensitivity of the employed sensors, is based on two steps: first, sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a `credibility index' is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination. View full abstract»

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  • Low-cost optimal state feedback fuzzy control of nonlinear second-order servo systems

    Publication Year: 2011 , Page(s): 1 - 4
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (716 KB) |  | HTML iconHTML  

    This paper discusses low-cost optimal Takagi-Sugeno state feedback fuzzy controllers for the position control of servo systems where the process is modeled by second-order linear dynamics with an integral component, and saturation and dead zone input static nonlinearity. The state feedback gain matrices in the rule consequents of the fuzzy controllers are obtained by the combination of the parallel distributed compensation and linear-quadratic regulator applied to each rule. An example concerning the position control of a DC servo system laboratory equipment is offered and experimental results are included. View full abstract»

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  • Experimental validation of recurrent Neuro-Fuzzy Networks for AFR estimation and control in SI engines

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (903 KB) |  | HTML iconHTML  

    An accurate model of Air to Fuel Ratio (AFR) dynamics is critical for high-quality AFR control in SI engines. These modeling and control problems are very sensitive because the dynamics of intake manifold air-fuel flow is severely nonlinear and multivariable. This study focuses on Recurrent Neuro-Fuzzy Network (RNFN) estimation and control of AFR nonlinear dynamics in SI engines. First, a nonlinear autoregressive with exogenous inputs (NARX) model is chosen for modeling the AFR nonlinear dynamics in the fuel injection system. Then, the strategy based on RNFN, is employed to fine-tune the model parameters. A controller is also designed based on inverse model-based method. The objective of control scheme is to keep the AFR constraint conditions by providing the proper fuel injection commands. This strategy is performed on an informative data-set obtained by a real-time in-vehicle experimental test. The effectiveness of the proposed approach is evaluated and validated by the resulting improvement in comparison with ECU performance. View full abstract»

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  • An evolving risk management framework for wireless sensor networks

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1222 KB) |  | HTML iconHTML  

    Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensor's overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration. View full abstract»

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