Journal articles: 'Performance of system identification techniques'

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Plummer, A. R., and N. D. Vaughan. "Discrete-Time System Identification for Electrohydraulic Servo Systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 209, no. 3 (August 1995): 165–77. http://dx.doi.org/10.1243/pime_proc_1995_209_381_02.

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The application of system identification to an electrohydraulic servo system is described. Identified models are used to design pole-placement controllers for the system. A variety of parameter estimators and model structure selection techniques are compared by assessing the performance of the controllers designed from the estimated models. The least squares method processing filtered data is found to yield reliable models, and an appropriate model structure can be selected successfully by comparing prediction errors for models of different structure.

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Zhang, E., R. Pintelon, and P. Guillaume. "Modal Identification Using OMA Techniques: Nonlinearity Effect." Shock and Vibration 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/178696.

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This paper is focused on an assessment of the state of the art of operational modal analysis (OMA) methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best linear approximation plus a term representing nonlinear distortions. As the nonlinear distortions are of stochastic nature and thus indistinguishable from the measurement noise, a protocol based on the use of the random phase multisine is proposed to reveal the accuracy and robustness of the linear OMA technique in the presence of the system nonlinearity. Several frequency- and time-domain based OMA techniques are examined for the modal identification of simulated and real nonlinear mechanical systems. Theoretical analyses are also provided to understand how the system nonlinearity degrades the performance of the OMA algorithms.

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Lin, Liu Hsu, Jai Yush Yen, and Fu Cheng Wang. "System Identification and Robust Control of a Pneumatic Muscle Actuator System." Applied Mechanics and Materials 284-287 (January 2013): 1936–40. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1936.

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This paper describes the application of system identification techniques and robust control strategies to a pneumatic muscle actuator system. Due to the inherent nonlinear and time-varying characteristics of this system, it is difficult to achieve excellent performance using conventional control methods. Therefore, we apply identification techniques to model the system as linear transfer functions and regard the un-modeled dynamics as system uncertainties. Because robust control is well-known for its capability in dealing with system uncertainties, we then apply robust control strategies to guarantee system stability and performance for the system. This work is carried out in three parts. First, the pneumatic muscle actuator system was modeled as linear transfer functions. Second, robust control theorem were utilized to design a Hinf robust controller to deal with system uncertainties and performance requirements. Finally, the designed controller was implemented for experimental verifications and compared with a conventional PID controller. From the experimental results, the proposed Hinf robust controller is deemed effective.

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Papadopoulos, Panagiotis N., Grigoris K. Papagiannis, Andrew J. Roscoe, Paul Crolla, Theofilos A. Papadopoulos, and Graeme M. Burt. "Measurement-based analysis of the dynamic performance of microgrids using system identification techniques." IET Generation, Transmission & Distribution 9, no. 1 (January 8, 2015): 90–103. http://dx.doi.org/10.1049/iet-gtd.2014.0555.

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Jayanna, H. S., and B. G. Nagaraja. "An Experimental Comparison of Modeling Techniques and Combination of Speaker – Specific Information from Different Languages for Multilingual Speaker Identification." Journal of Intelligent Systems 25, no. 4 (October 1, 2016): 529–38. http://dx.doi.org/10.1515/jisys-2014-0128.

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AbstractMost of the state-of-the-art speaker identification systems work on a monolingual (preferably English) scenario. Therefore, English-language autocratic countries can use the system efficiently for speaker recognition. However, there are many countries, including India, that are multilingual in nature. People in such countries have habituated to speak multiple languages. The existing speaker identification system may yield poor performance if a speaker’s train and test data are in different languages. Thus, developing a robust multilingual speaker identification system is an issue in many countries. In this work, an experimental evaluation of the modeling techniques, including self-organizing map (SOM), learning vector quantization (LVQ), and Gaussian mixture model-universal background model (GMM-UBM) classifiers for multilingual speaker identification, is presented. The monolingual and crosslingual speaker identification studies are conducted using 50 speakers of our own database. It is observed from the experimental results that the GMM-UBM classifier gives better identification performance than the SOM and LVQ classifiers. Furthermore, we propose a combination of speaker-specific information from different languages for crosslingual speaker identification, and it is observed that the combination feature gives better performance in all the crosslingual speaker identification experiments.

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McKelvey, Tomas, Andrew Fleming, and S. O. Reza Moheimani. "Subspace-Based System Identification for an Acoustic Enclosure." Journal of Vibration and Acoustics 124, no. 3 (June 12, 2002): 414–19. http://dx.doi.org/10.1115/1.1467653.

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This paper is aimed at identifying a dynamical model for an acoustic enclosure, a duct with rectangular cross section, closed ends, and side-mounted speaker enclosures. Acoustic enclosures are known to be resonant systems of high order. In order to design a high performance feedback controller for an acoustic enclosure, one needs to have an accurate model of the system. Subspace-based system identification techniques have proven to be an efficient means of identifying dynamics of high order highly resonant systems. In this paper a frequency domain subspace-based method together with a second iterative optimization step minimizing a frequency domain least-squares criterion is successfully employed to identify a dynamical model for an acoustic enclosure.

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Hamd, Muthana Hachim. "Optimized biometric system based iris-signature for human identification." International Journal of Advances in Intelligent Informatics 5, no. 3 (October 29, 2019): 273. http://dx.doi.org/10.26555/ijain.v5i3.407.

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This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy.

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Fateh, Rachid, Anouar Darif, and Said Safi. "Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method." Journal of Telecommunictions and Information Technology 4, no. 2021 (December 30, 2021): 1–11. http://dx.doi.org/10.26636/jtit.2021.151621.

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Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channels.

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Gonzales, Oscar. "Parametric and Non-parametric Mathematical Modelling Techniques: A Practical Approach of an Electrical Machine Identification." Ecuadorian Science Journal 5, no. 1 (March 31, 2021): 30–36. http://dx.doi.org/10.46480/esj.5.1.86.

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Mathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures.

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Rituerto-González, Esther, Alba Mínguez-Sánchez, Ascensión Gallardo-Antolín, and Carmen Peláez-Moreno. "Data Augmentation for Speaker Identification under Stress Conditions to Combat Gender-Based Violence." Applied Sciences 9, no. 11 (June 4, 2019): 2298. http://dx.doi.org/10.3390/app9112298.

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A Speaker Identification system for a personalized wearable device to combat gender-based violence is presented in this paper. Speaker recognition systems exhibit a decrease in performance when the user is under emotional or stress conditions, thus the objective of this paper is to measure the effects of stress in speech to ultimately try to mitigate their consequences on a speaker identification task, by using data augmentation techniques specifically tailored for this purpose given the lack of data resources for this condition. An extensive experimentation has been carried out for assessing the effectiveness of the proposed techniques. First, we conclude that the best performance is always obtained when naturally stressed samples are included in the training set, and second, when these are not available, their substitution and augmentation with synthetically generated stress-like samples improves the performance of the system.

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Slavov, Tsonyo, Alexander Mitov, and Jordan Kralev. "Advanced Embedded Control of Electrohydraulic Power Steering System." Cybernetics and Information Technologies 20, no. 2 (June 1, 2020): 105–21. http://dx.doi.org/10.2478/cait-2020-0020.

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AbstractThe article presents a developed embedded system for control of electrohydraulic power steering based on multivariable uncertain plant model and advanced control techniques. The plant model is obtained by identification procedure via “black box” system identification and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the model. Three types of controller are designed: Linear-Quadratic Gaussian (LQG) controller, H∞ controller and μ-controller. The main result is a performed comparative analysis of time and frequency domain properties of control systems. The results show the better performance of systems based on µ-controllers. Also the robust stability and robust performance are investigated. All three systems achieved robust stability which guarantees their workability, but only the system with µ-controller has robust performance against prescribed uncertainties. The control algorithms are implemented in specialized 32-bit microcontroller. A number of real world experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.

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Lassoued, Zeineb, and Kamel Abderrahim. "New Approaches to Identification of PWARX Systems." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/845826.

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We consider the clustering-based procedures for the identification of discrete-time hybrid systems in the piecewise affine (PWA) form. These methods exploit three main techniques which are clustering, linear identification, and pattern recognition. The clustering method based on thek-means algorithm is treated in this paper. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition while knowing the model ordersnaandnband the number of submodelss. The performance of this approach can be threatened by the presence of outliers and poor initializations. To overcome these problems, we propose new techniques for data classification. The proposed techniques exploit Chiu’s clustering technique and the self-artificial Kohonen neural network approach in order to improve the performance of both the clustering and the final linear regression procedure. Simulation results are presented to illustrate the performance of the proposed method.

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Henderson, M., M. Ingleby, and D. Ford. "Robust system identification using the Hough transform." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 2 (March 1, 1997): 135–44. http://dx.doi.org/10.1243/0959651971539957.

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The Hough transform provides a basis for robust extraction of shapes and continues to attract the interest of the image processing community. It has robustness properties desirable in many applications of pattern recognition, including parameter estimation. These properties are robustness to impulsive noise, insensitivity to partial occlusion of patterns, graceful degradation of performance in the presence of Gaussian and impulsive noise, as well as the ability to respond to concurrent patterns coexisting in the same data source. This technique has been applied to the problem of parameter estimation for linear and non-linear models. A critical comparison is made with the more traditional least-squares-based parameter estimators and it is argued that transform-based techniques are, in certain circumstances, more suitable for real-time intelligent control than those currently in use.

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Hussin, A. S., A. R. Abdullah, M. H. Jopri, T. Sutikno, N. M. Saad, and Weihown Tee. "Harmonic Load Diagnostic Techniques and Methodologies: A Review." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 690. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp690-695.

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<p>This paper will review on the existing techniques and methodologies of harmonic load diagnostic system. The increasingly amount of harmonic producing load used in power system are the main contribution in quantifying each harmonic disturbance effects of the multiple harmonic producing loads and it became very important. Literature proposes two different techniques and methods on the harmonic source identification under the soft computing technique classification. The advantages and disadvantages of harmonic load identification techniques and methods are discussed in this paper. In the proposed method, the issue on the harmonic contribution is determine and transformed to a data correlation analysis. Several techniques to identify the sources of harmonic signals in electric power systems are described and reviewed based on previous paper. Comparative studies of the methods are also done to evaluate the performance of each techniques. However, without sufficient information in this inconsistent environment on the property of the power system, accurate harmonic producing load diagnosis methods are important and further investigations in this regard assumes great implication.</p>

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Kim, Kwang-il, and Keon Myung Lee. "Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data." Applied Sciences 10, no. 11 (June 10, 2020): 4010. http://dx.doi.org/10.3390/app10114010.

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Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

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Rahman, Motiar, Subramania Sudharsanan, Kanthasamy K. Muraleetharan, and Musharraf M. Zaman. "Modeling Time-Dependent Behavior of Pavement Drainage Using Linear System Identification and Neural Network Techniques." Transportation Research Record: Journal of the Transportation Research Board 1582, no. 1 (January 1997): 34–41. http://dx.doi.org/10.3141/1582-06.

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Inadequate drainage continues to be a major cause of problems associated with long-term structural integrity and performance of roadway pavements. To reduce the impact of these drainage-related problems, it is customary to provide measures to prevent water from entering the pavement system and to enhance the drainage capability of the pavement base to rapidly move the water that inevitably finds its way into the pavement system. The performance of different drainage systems in the field is not clearly known. Oklahoma Department of Transportation (ODOT) has been collecting rainfall and outflow information at sites with edge drains since 1992. The sites have different types of surfaces, base courses, and edge drains. The data collected by ODOT at these sites were used to develop two types of numerical models to predict the outflow–time history using rainfall–time history as the input. One model is based on linear system identification theory, and the other model is based on an artificial neural network. The development of these models is presented, and the model predictions are compared with the measured field data. The efficiency of the drainage systems, including the AASHTO criteria for the drainage time, at these sites is compared by using the numerical models and synthetic, but the same, rainfall events.

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Zhang, Wancheng, and Patrick A. Naylor. "An Algorithm to Generate Representations of System Identification Errors." Research Letters in Signal Processing 2008 (2008): 1–4. http://dx.doi.org/10.1155/2008/529291.

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An algorithm to generate representations of system identification (SI) errors, which enables systematic testing of the performance of system equalization techniques, is proposed. With this algorithm, the normalized projection misalignment (NPM) of the generated error representation can be chosen to suit the particular characteristics of the application under test. Additionally, the generated error representation can represent all the error vectors corresponding to different scaling factors in the estimates of the system impulse response (SIR), without influencing the signal-to-distortion ratio (SDR) of the equalized impulse response.

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Kumar, V. K. Narendira, and B. Srinivasan. "Performance of Personal Identification System Technique Using Iris Biometrics Technology." International Journal of Image, Graphics and Signal Processing 5, no. 5 (April 16, 2013): 63–71. http://dx.doi.org/10.5815/ijigsp.2013.05.08.

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Cavalcanti, Anderson L. O., Karl H. Kienitz, and Visakan Kadirkamanathan. "Identification of Two-Time Scaled Systems Using Prefilters." Journal of Control Science and Engineering 2018 (October 3, 2018): 1–10. http://dx.doi.org/10.1155/2018/3138149.

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This paper deals with the identification of two-time scale linear dynamic systems, which are an important class of multiscale systems. Classical identification processes may fail to yield accurate parameters for systems of this class and, for this reason, the authors propose two different techniques to estimate the system parameters. The first technique utilizes two prefilters that are iteratively tuned. The second one considers wavelet filters that are tuned based on the results of the first iterative algorithm. Identification and analysis results for a dynamical aircraft model are shown to demonstrate the algorithm’s performance.

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Robles-Algarín, Carlos, Omar Rodríguez, and Adalberto Ospino. "Evaluation of non-parametric identification techniques in second order models plus dead time." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6340. http://dx.doi.org/10.11591/ijece.v10i6.pp6340-6348.

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In this paper, a set of non-parametric identification techniques are used in order to obtain second order models plus dead time for an underdamped system. Initially, non-parametric techniques were used to identify the system from the temperature data of a coal-heated oven. In this case, the identification techniques proposed by Stark, Jahanmiri - Fallahi and Ogata were used, which require obtaining two or three points of the step response for the system under study. In addition, the Matlab PID Tuner app was used to identify the underdamped system and compare the results with the other methods. The results show that the PID Tuner and the method proposed by Ogata are the ones that best represent the dynamics of the underdamped system, taking into account the values for the Integral Absolute Error (IAE) and the correlation coefficient. With the Stark method an IAE of 181.56 was obtained, while with the PID Tuner the best performance was achieved with an IAE of 21.59. In terms of the results obtained with the cross correlation, the best performance was achieved with the PID tuner and the Stark method.

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Tang, Jing, Yongheng Yang, Frede Blaabjerg, Jie Chen, Lijun Diao, and Zhigang Liu. "Parameter Identification of Inverter-Fed Induction Motors: A Review." Energies 11, no. 9 (August 22, 2018): 2194. http://dx.doi.org/10.3390/en11092194.

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Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

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Juillet, Fabien, Peter J. Schmid, and Patrick Huerre. "Control of amplifier flows using subspace identification techniques." Journal of Fluid Mechanics 725 (May 17, 2013): 522–65. http://dx.doi.org/10.1017/jfm.2013.194.

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AbstractA realistic, efficient and robust technique for the control of amplifier flows has been investigated. Since this type of fluid system is extremely sensitive to upstream environmental noise, an accurate model capturing the influence of these perturbations is needed. A subspace identification algorithm is not only a convenient and effective way of constructing this model, it is also realistic in the sense that it is based on input and output data measurements only and does not require other information from the detailed dynamics of the fluid system. This data-based control design has been tested on an amplifier model derived from the Ginzburg–Landau equation, and no significant loss of efficiency has been observed when using the identified instead of the exact model. Even though system identification leads to a realistic control design, other issues such as state estimation, have to be addressed to achieve full control efficiency. In particular, placing a sensor too far downstream is detrimental, since it does not provide an estimate of incoming perturbations. This has been made clear and quantitative by considering the relative estimation error and, more appropriately, the concept of a visibility length, a measure of how far upstream a sensor is able to accurately estimate the flow state. It has been demonstrated that a strongly convective system is characterized by a correspondingly small visibility length. In fact, in the latter case the optimal sensor placement has been found upstream of the actuators, and only this configuration was found to yield an efficient control performance. This upstream sensor placement suggests the use of a feed-forward approach for fluid systems with strong convection. Furthermore, treating upstream sensors as inputs in the identification procedure results in a very efficient and robust control. When validated on the Ginzburg–Landau model this technique is effective, and it is comparable to the optimal upper bound, given by full-state control, when the amplifier behaviour becomes convection-dominated. These concepts and findings have been extended and verified for flow over a backward-facing step at a Reynolds number $\mathit{Re}= 350$. Environmental noise has been introduced by three independent, localized sources. A very satisfactory control of the Kelvin–Helmholtz instability has been obtained with a one-order-of-magnitude reduction in the averaged perturbation norm. The above observations have been further confirmed by examining a low-order model problem that mimics a convection-dominated flow but allows the explicit computation of control-relevant measures such as observability. This study casts doubts on the usefulness of the asymptotic notion of observability for convection-dominated flows, since such flows are governed by transient effects. Finally, it is shown that the feed-forward approach is equivalent to an optimal linear–quadratic–Gaussian control for spy sensors placed sufficiently far upstream or for sufficiently convective flows. The control design procedure presented in this paper, consisting of data-based subspace identification and feed-forward control, was found to be effective and robust. Its implementation in a real physical experiment may confidently be carried out.

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Mohammed, Yosra Abdulaziz. "Infant Cry Recognition System." International Journal of Advanced Pervasive and Ubiquitous Computing 11, no. 1 (January 2019): 15–32. http://dx.doi.org/10.4018/ijapuc.2019010102.

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Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.

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Nikkam, Pushpalatha Shrikant, and B. Eswara Reddy. "A Key Point Selection Shape Technique for Content Based Image Retrieval System." International Journal of Computer Vision and Image Processing 6, no. 2 (July 2016): 54–70. http://dx.doi.org/10.4018/ijcvip.2016070104.

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Content Based Image Retrieval (CBIR) is the process of retrieving visually similar images from huge datasets. Images are identified based on their content. Content identification using shape features is considered in this paper. Content identification using shapes is a challenging task considering multiple variations observed in images, complex backgrounds and vast categories of contents. This paper describes a shape descriptor based CBIR system. The content of an image is identified using a key point based shape descriptor. Template matching techniques are adopted to accurately describe object shapes. The object shape identified is described using histogram vectors. The use of SVM classifier for content recognition and image retrieval task is considered. Results presented prove robustness of the key point technique to accurately describe object shapes even in complex images. Performance of the proposed system is compared with existing state of art systems. Results obtained and described in the paper prove a better performance of proposed CBIR system.

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Moriot, Jérémy, Nicolas Quaegebeur, Alain Le Duff, and Patrice Masson. "A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques." Structural Health Monitoring 17, no. 6 (December 4, 2017): 1460–72. http://dx.doi.org/10.1177/1475921717744679.

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This article aims at providing a framework for assessing the detection and localization performance of guided wave–based structural health monitoring imaging systems. The assessment exploits a damage identification metric providing a diagnostic of the structure from an image of the scatterers generated by the system, allowing detection, localization, and size estimation of the damage. Statistical probability of detection and probability of localization curves are produced based on values of the damage identification metric for several damage sizes and positions. Instead of relying on arduous measurements on a significant number of structures instrumented in the same way, a model-based approach is considered in this article for estimating probability of detection and probability of localization curves numerically. This approach is first illustrated in a simplistic model, which allows characterizing the robustness of the structural health monitoring system for various levels of noise in test signals. An experimental test case using a more realistic case with an artificial damage is then considered for validating the approach. A good agreement between experimental and numerical values of the damage identification metric and derived probability of detection and probability of localization curves is observed.

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Mohammed, Faris E., Dr Eman M. ALdaidamony, and Prof A. M. Raid. "IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES." Journal of advanced Sciences and Engineering Technologies 1, no. 2 (May 21, 2018): 34–44. http://dx.doi.org/10.32441/jaset.v1i2.119.

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Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems. © 2018 JASET, International Scholars and Researchers Association

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S, Adebayo, Ogunti E.O, and Awofolaju T.T. "Performance Evaluation of some Modulation Techniques for Radio Frequency Identification (RFID) System under different communication channels." IOSR Journal of Electronics and Communication Engineering 9, no. 3 (2014): 90–97. http://dx.doi.org/10.9790/2834-09349097.

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Wang, Jiandong, Jianjun Su, Yan Zhao, Xiangkun Pang, Jun Li, and Zhenfu Bi. "Performance assessment of primary frequency control responses for thermal power generation units using system identification techniques." International Journal of Electrical Power & Energy Systems 100 (September 2018): 81–90. http://dx.doi.org/10.1016/j.ijepes.2018.02.036.

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Lu, L., G. D. Padfield, M. White, and P. Perfect. "Fidelity enhancement of a rotorcraft simulation model through system identification." Aeronautical Journal 115, no. 1170 (August 2011): 453–70. http://dx.doi.org/10.1017/s0001924000006102.

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AbstractHigh fidelity modelling and simulation are prerequisites for ensuring confidence in decision making during aircraft design and development, including performance and handling qualities, control law developments, aircraft dynamic loads analysis, and the creation of a realistic simulation environment. The techniques of system identification provide a systematic framework for ‘enhancing’ a physics–based simulation model derived from first principles and aircraft design data. In this paper we adopt a frequency domain approach for model enhancement and fidelity improvement of a baseline FLIGHTLAB Bell 412 helicopter model developed at the University of Liverpool. Predictability tests are based on responses to multi–step control inputs. The techniques have been used to generate one, three, and six degree-of-freedom linear models, and their derivatives and predictability are compared to evaluate and augment the fidelity of the FLIGHTLAB model. The enhancement process thus involves augmenting the simulation model based on the identified parameters. The results are reported within the context of the rotorcraft simulation fidelity project, Lifting Standards, involving collaboration with the Flight Research Laboratory (NRC, Ottawa), supported with flight testing on the ASRA research helicopter.

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Mohammed, Rawia A., Nidaa F. Hassan, and Akbas E. Ali. "Arabic Speaker Identification System Using Multi Features." Engineering and Technology Journal 38, no. 5A (May 25, 2020): 769–78. http://dx.doi.org/10.30684/etj.v38i5a.408.

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The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase. The second phase is testing, which searches the identification of the speaker. In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency. Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process. The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8%.

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Kumar, M. Senthil, and K. Mahadevan. "Performance Comparison of Moisture Control in Paper Industry Using Soft Computing Techniques." Applied Mechanics and Materials 573 (June 2014): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amm.573.322.

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In this paper, Genetic Algorithm (GA) method has been applied in the moisture control system for auto tuning (PID) parameters. Proportional – Integral – Derivatives control scheme is used to provide an efficient and quiet easier in control engineering applications. Most of the PID tuning methods are used as manually which is difficult and time consuming. Genetic Algorithm which leads to improve the efficiency of tuning of process. The proposed algorithm is used to tune the PID parameters and its performance has been compared with Fuzzy logic techniques.Compare to the fuzzy logic technique dynamic performance specfications such as rise time, peak time and peak overshoot optimal values produced by GA. The plant model represented by the transfer function is obtained by the system identification tool box.

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Alabbasi, Hesham A., Ali M. Jalil, and Fadhil S. Hasan. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5208. http://dx.doi.org/10.11591/ijece.v10i5.pp5208-5216.

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.

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Maheskumar, Mr V. "Building Crack Detection Using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2330–38. http://dx.doi.org/10.22214/ijraset.2022.44303.

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Abstract: Reliability, performance, and life cycle costs are real concerns for almost all in-service massive structures, such as buildings, bridges, nuclear facilities, hydroelectric structures, and dams. Cracks on these structures are a common phenomenon associated with various internal and external forces, including the corrosion of embedded reinforcement, chemical deterioration of concrete, and the application of adverse loading to the structure. In comparison to the traditional manual inspection-based crack detection system, computer vision and machine learning-based approaches are quickly becoming an integral part of the modern segmentation of civil infrastructures to automate crack detection and identification systems. The project is about the construction and application of a device that uses image processing to detect cracks. The system has a graphical user interface for initializing the device, viewing real time image, taking pictures of a crack, measuring its width, and evaluating if safe or unsafe.

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Alabsi, Mohammed, and Travis Fields. "Quadrotor aircraft intelligent system identification experiment design." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 13 (March 6, 2019): 4911–25. http://dx.doi.org/10.1177/0954410019833209.

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Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft model development and prototyping approaches with an efficient real time paradigm. The work presented herein describes the development of an intelligent excitation input design technique that determines excitation frequencies based on predefined rotational motion dynamic model. The input design is then evaluated on quadcopter unmanned aircraft that utilizes the new multisine input design. In order to minimize flight excursions without compromising the modeling capabilities, multisine input power spectrum is optimized based on the vehicle’s frequency response. The proposed methodology emphasizes excitation of modal frequencies which yields flight data rich information content. The generated optimized multisine input design is utilized for a quadcopter aircraft system identification and the performance is compared to conventional uniform amplitudes design. Simulation results show highly accurate model estimation in all identification results in addition to reduction of induced perturbations and power consumption. Additionally, the generated model prediction capabilities are not compromised after power spectrum optimization. Overall, the proposed technique introduces an efficient and intelligent system identification experiment design that can minimize the time and effort spent during excitation input design.

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Czarnul, Pawel, Jerzy Proficz, and Adam Krzywaniak. "Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments." Scientific Programming 2019 (April 24, 2019): 1–19. http://dx.doi.org/10.1155/2019/8348791.

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The paper presents state of the art of energy-aware high-performance computing (HPC), in particular identification and classification of approaches by system and device types, optimization metrics, and energy/power control methods. System types include single device, clusters, grids, and clouds while considered device types include CPUs, GPUs, multiprocessor, and hybrid systems. Optimization goals include various combinations of metrics such as execution time, energy consumption, and temperature with consideration of imposed power limits. Control methods include scheduling, DVFS/DFS/DCT, power capping with programmatic APIs such as Intel RAPL, NVIDIA NVML, as well as application optimizations, and hybrid methods. We discuss tools and APIs for energy/power management as well as tools and environments for prediction and/or simulation of energy/power consumption in modern HPC systems. Finally, programming examples, i.e., applications and benchmarks used in particular works are discussed. Based on our review, we identified a set of open areas and important up-to-date problems concerning methods and tools for modern HPC systems allowing energy-aware processing.

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Zwemer, Matthijs H., Herman G. J. Groot, Rob Wijnhoven, Egor Bondarev, and Peter H. N. de With. "Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques." Sensors 21, no. 14 (July 7, 2021): 4659. http://dx.doi.org/10.3390/s21144659.

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This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.

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Lin, Liu-Hsu, Jia-Yush Yen, and Fu-Cheng Wang. "ROBUST CONTROL FOR A PNEUMATIC MUSCLE ACTUATOR SYSTEM." Transactions of the Canadian Society for Mechanical Engineering 37, no. 3 (September 2013): 581–90. http://dx.doi.org/10.1139/tcsme-2013-0046.

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This paper presents the modeling and robust control of a pneumatic muscle actuator system. Due to the inherent nonlinear and time-varying characteristics of this system, it is difficult to achieve excellent performance using conventional control methods. Therefore, we apply identification techniques to model the system as linear transfer functions and regard the un-modeled dynamics as system uncertainties. Because H∞ robust control is well-known for its capability in dealing with system uncertainties, we then apply H∞ robust control strategies to guarantee system stability and performance for the system. From the experimental results, the proposed H∞ robust controller is deemed effective.

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Chen, Yung-Yao, Sin-Ye Jhong, Chih-Hsien Hsia, and Kai-Lung Hua. "Explainable AI: A Multispectral Palm-Vein Identification System with New Augmentation Features." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3s (October 31, 2021): 1–21. http://dx.doi.org/10.1145/3468873.

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Recently, as one of the most promising biometric traits, the vein has attracted the attention of both academia and industry because of its living body identification and the convenience of the acquisition process. State-of-the-art techniques can provide relatively good performance, yet they are limited to specific light sources. Besides, it still has poor adaptability to multispectral images. Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, they often require large training samples and high computation that are infeasible for palm-vein identification. To address this limitation, this work proposes a palm-vein identification system based on lightweight CNN and adaptive multi-spectral method with explainable AI. The principal component analysis on symmetric discrete wavelet transform (SMDWT-PCA) technique for vein images augmentation method is adopted to solve the problem of insufficient data and multispectral adaptability. The depth separable convolution (DSC) has been applied to reduce the number of model parameters in this work. To ensure that the experimental result demonstrates accurately and robustly, a multispectral palm image of the public dataset (CASIA) is also used to assess the performance of the proposed method. As result, the palm-vein identification system can provide superior performance to that of the former related approaches for different spectrums.

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P.S, Abdul Lateef Haroon, and U. Eranna. "An Efficient Activity Detection System based on Skeleton Joints Identification." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4995. http://dx.doi.org/10.11591/ijece.v8i6.pp4995-5003.

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The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions.

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Lee, Hsi Chieh, and Shao Hsuan Chang. "IC Assembly Product Inspection Using Image Processing Techniques." Applied Mechanics and Materials 145 (December 2011): 209–13. http://dx.doi.org/10.4028/www.scientific.net/amm.145.209.

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In this study, we present an image identification and measurement system for examining and testing the packaged semiconductor specifications. Most semiconductor processes rely on measurement gauges or inspectors to examine the finished product specifications visually. Measurement gauges are costly; inspectors have to be trained professionally and their performance depends on the maturity of their skills, which requires enormous costs, time, and efforts. Therefore, an automatic identification and measurement system will not only reduce costs, but minimize the complexity of measurement, thereby upgrading the effectiveness of manpower significantly. Experiments were conducted using 1657 BMP images with 640 * 480 pixels taken with a semiconductor machine. These images are snapshots randomly taken from of a variety of the 7.2 cm * 4.8 cm Substrate circuit boards each includes 180 pieces of 30 * 6 IC packaged products. Promising results were derived where 1492 out of 1657 product images were successfully detected and measured. In addition to the 90.04% success rate for inspection, the process time is reduced significantly to about 1/6 as compared to human professionals.

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Chinnathambi, Kalaiselvi. "Detection and Classification of Leukemia using MPFCM Segmentation and Random Forest with Boosting Techniques." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 1 (September 19, 2018): 5933–39. http://dx.doi.org/10.24297/jac.v13i1.4656.

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Identification of blood disorders is through visual inspection of microscopic blood cell images. From the identification of blood disorders lead to classification of certain diseases related to blood. We propose an automatic segmentation method for segmenting White blood cell images. Firstly, modified possibilistic fuzzy c-means algorithm is proposed to detect the contours in the image. The GLCM features are extracted and features are selected by MRMR. Adaptive boosting and LS Boosting has been utilized to classify blast cells from normal lymphocyte cells. Comparison performance of classification accuracy was carried out. The effectiveness of the classification system is tested with the total of 80 samples collected. The evaluated results demonstrate that our method outperformed the existing systems with an accuracy of 88 %.

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Kondo, Ricardo Eiji, Eduardo de Freitas Rocha Loures, Eduardo Alves Portela Santos, and Carmela Maria Polito Braga. "Alarm Rationalization Based on Process Mining Techniques." Advanced Materials Research 1061-1062 (December 2014): 1258–65. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.1258.

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Industries in general need a reliable system for fault identification and alarm management. The high incidence of alarms can overload the operator, exposing him to conditions that may exceed their ability to perform effective actions, impairing his performance during his workday. In this context, the present paper proposes an approach for alarms rationalization based on process mining techniques. The alarm rationalization is in accordance to the alarm lifecycle management model suggested by ANSI / ISA-18 standard. The aim of this paper is to improve the alarm system through its rationalization, allowing an adequate organization and data layout of the whole set of alarms. Thus, it is expected a better interpretation and understanding of the industrial process. Performance metrics recommended by ANSI / ISA-18 standard are used. Such metrics are used for analyzing a database of alarms coming from an industrial plant. Preliminaries results have demonstrated the feasibility of the present approach. The results show that the use of the process mining technique can provide support on rationalization alarms, standing for a promising method in alarm management domain.

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Al Mamun, Md, and Mohammad Shorif Uddin. "A Survey on a Skin Disease Detection System." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (October 2021): 1–17. http://dx.doi.org/10.4018/ijhisi.20211001.oa35.

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Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

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Camargo, Edgar, and Jose Aguilar. "Advanced Supervision Of Oil Wells Based On Soft Computing Techniques." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (July 1, 2014): 215–25. http://dx.doi.org/10.1515/jaiscr-2015-0010.

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Abstract In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production. Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.

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Lumbers, J. P., and R. Y. G. Andoh. "The Identification of Benthic Feed-Back in Facultative Ponds." Water Science and Technology 19, no. 12 (December 1, 1987): 177–82. http://dx.doi.org/10.2166/wst.1987.0143.

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An identification study of the feed-back of soluble organic matter from the benthic sludge in facultative ponds is described, based on field data reported for the New Mexico pond system in the USA. The study employed recursive estimation techniques to aid the identification of appropriate model structures. Model development progressed from the analysis of a simple non-reactive system to one incorporating two sub-systems, namely the planktonic region and the benthic region. It was found that the inclusion of a temperature-related feed-back term produced good model results while the temperature-corrected decay rate constant remained constant over time. The magnitude of the feed-back term would appear to be equal to the incoming load in the hottest months of the year. The implications for performance evaluation and design are discussed.

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Sureshbabu, P., and M. Sakthivadivu. "A Review on Biometrics Authentication System Using Fingerprint." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 4–6. http://dx.doi.org/10.51983/ajcst-2019.8.s1.2016.

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Technology based on Biometric identification and verification is one leading research area. It deals with the concept analyzing the human body characteristics through Biometric devices for various authentications process. There are many Biometric authentication systems are available for verification process. This paper discusses the role of Fingerprint authentication. FP recognition is highly used biometric technique, because of abundance sources (i.e. ten fingers) availability for collecting data. Discussion on Fingerprint matching techniques, recognition methods and their performance analysis are made in the paper.

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Elvin Abdullayev, Elvin Abdullayev. "OVERHEAD ALLOCATION TECHNIQUES AND FINANCIAL PERFORMANCE." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 18, no. 07 (May 27, 2022): 55–63. http://dx.doi.org/10.36962/pahtei18072022-55.

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In recent years sales of cellular connection has become the fastest growing industry and one of the major areas of domestic and global telecommunications markets. The rapid growth of information flows requires accelerated development of the cellular market and continuous improvement methodology, economic theory, and methods of analysis and management in this area. The most important tasks of modern management practices are to develop and implement decisions to achieve financial and economic stability and efficiency of the organization.In this regard, there are problems with the need to develop, apply, and perspective development of theoretical-methodological and administrative - actual methods to cost analysis and product costs in the economic activities of mobile companies, which determines the relevance of the research topic. All this leads to problems associated with the need to develop theoretical and methodological, organizational, and practical approaches of management analysis of cellular companies in the modern economy of our country and determines the relevance of the research topic. From a cost management standpoint, the research suggests that manufacturing organizations employ the ABC method since it allocates overhead expenses more correctly than alternative cost accounting systems. Adopting this approach will result in: a) a better knowledge of the business processes that drive costs, b) a better technique for identifying productivity improvements that improve service delivery, and c) a clear identification of the performance metrics that improve cost management. These are critical elements of continuous improvement and comprehensive quality management programs that are critical to achieving an organization's objectives, goals, and, ultimately, financial success. Keywords: over head, costs, economic development

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Fatima, Bahjat, Huma Ramzan, and Sohail Asghar. "Session identification techniques used in web usage mining." Online Information Review 40, no. 7 (November 14, 2016): 1033–53. http://dx.doi.org/10.1108/oir-08-2015-0274.

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Purpose The purpose of this paper is to critically analyze the state-of-the-art session identification techniques used in web usage mining (WUM) process in terms of their limitations, features, and methodologies. Design/methodology/approach In this research, systematic literature review has been conducted using review protocol approach. The methodology consisted of a comprehensive search for relevant literature over the period of 2005-2015, using four online database repositories (i.e. IEEE, Springer, ACM Digital Library, and ScienceDirect). Findings The findings revealed that this research area is still immature and existing literature lacks the critical review of recent session identification techniques used in WUM process. Originality/value The contribution of this study is to provide a structured overview of the research developments, to critically review the existing session identification techniques, highlight their limitations and associated challenges and identify areas where further improvements are required so as to complement the performance of existing techniques.

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Tahir, Muhammad, and Saeed Anwar. "Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System." Applied Sciences 11, no. 19 (October 2, 2021): 9197. http://dx.doi.org/10.3390/app11199197.

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Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks.

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Ishizaka, Alessio, and Vijay Edward Pereira. "Portraying an employee performance management system based on multi-criteria decision analysis and visual techniques." International Journal of Manpower 37, no. 4 (July 4, 2016): 628–59. http://dx.doi.org/10.1108/ijm-07-2014-0149.

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Purpose – Performance appraisal is one of the most critical and indispensable human resource practices for organisations. However, it generates dissatisfaction among employees as it is often viewed as complex and ineffective. The purpose of this paper is to present a new performance management system that integrates multi-criteria decision analysis (MCDA) methods – the analytic network process (ANP) and PROMETHEE – with the visual techniques of the GAIA plane and the stacked bar chart. MCDA methods allow a structured and consistent evaluation integrating qualitative and quantitative criteria. Design/methodology/approach – The authors developed a structured and transparent performance management system. It is based on the MCDA methods PROMETHEE and ANP. It also incorporates the visual techniques: GAIA and stacked bar chart. Feedback for trainings and developments can precisely be formulated. Findings – Visual techniques permit clear identification and quantification, for each employee, of the areas that need improvement through training and development, which contributes to the resource-based view of organisations. A real case study has been portrayed to show the added value of the MCDA methods and the visual techniques in employee performance management. Originality/value – The paper describes a new employee performance system adopted in an organisation. The multi-criteria analysis transparently combines qualitative and quantitative decision criteria into a holistic and transparent evaluation. The visual techniques permit us to gain a deep insight into the employees’ skills profile and capture fine details where individuals perform or underperform.

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Journal articles on the topic 'Performance of system identification techniques'

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