IEEE Organizations related to Systems Support

Back to Top

No organizations are currently tagged "Systems Support"



Conferences related to Systems Support

Back to Top

No conferences are currently tagged "Systems Support"


Periodicals related to Systems Support

Back to Top

No periodicals are currently tagged "Systems Support"


Most published Xplore authors for Systems Support

Back to Top

No authors for "Systems Support"


Xplore Articles related to Systems Support

Back to Top

System Functions and Development

Successful Service Design for Telecommunications: A comprehensive guide to design and implementation, None

This chapter contains sections titled:IntroductionInterrelationships Between the Functional Areas in the Systems DomainCustomer Creation, Order Management and Service TerminationCustomer Network Provisioning and Network TerminationCustomer Service Provisioning (Including Moving, Additions and Changes)End‐User Creation and Order ManagementEnd‐User Network ProvisioningEnd‐User Service Provisioning, Service Control (Especially in QoS‐Based Services) and Service TerminationBilling, Charging and RatingService Accounting, Revenue Reporting, OLO Bill Reconciliation and Revenue AssuranceFault ...


A novel direct torque control for permanent magnet synchronous motor drive

2008 International Conference on Electrical Machines and Systems, 2008

To solve the problems of torque ripple and inconstant switch frequency of inverter in the conventional direct torque control (DTC) for permanent magnet synchronous motor (PMSM) drive, a novel DTC method using space vector pulse width modulation (SVM) is proposed based on analysis of PMSM mathematical model. In this novel DTC system of PMSM , traditional torque hysteresis controller and ...


Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016

The application of decision making knowledge based methods is to analyze the system and identify its in-depth diagnosis and fault behavior by simulating the expert knowledge from a similar domain. Rule based systems with predefined conditions have been replaced and or upgraded to expert knowledge based systems and further replaced / upgraded by applying machine learning techniques wherein, association rules, ...


Non-Invasive In-Home Sleep Stage Classification Using a Ballistocardiography Bed Sensor

2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019

Longitudinal monitoring of sleep related parameters can be used for early detection of diseases and also as an indication to physicians for effective adjustment of medication and dosage treatments for people at risk. The correlation between sleep disorders and health conditions such as Alzheimer's and Parkinson's diseases has already been reported in the literature. In this paper, we propose the ...


Algorithm of Shot Detection Based on SVM with Modified Kernel Function

2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009

Improving the precision of shot boundary detection is very important. This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into ...


More Xplore Articles

Educational Resources on Systems Support

Back to Top

IEEE-USA E-Books

  • System Functions and Development

    This chapter contains sections titled:IntroductionInterrelationships Between the Functional Areas in the Systems DomainCustomer Creation, Order Management and Service TerminationCustomer Network Provisioning and Network TerminationCustomer Service Provisioning (Including Moving, Additions and Changes)End‐User Creation and Order ManagementEnd‐User Network ProvisioningEnd‐User Service Provisioning, Service Control (Especially in QoS‐Based Services) and Service TerminationBilling, Charging and RatingService Accounting, Revenue Reporting, OLO Bill Reconciliation and Revenue AssuranceFault ManagementNetwork Management (Monitoring and Collecting Events from the Network) and Service ManagementPerformance ManagementCapacity Management, Traffic Management and Network PlanningReportingSystem Support and Management

  • A novel direct torque control for permanent magnet synchronous motor drive

    To solve the problems of torque ripple and inconstant switch frequency of inverter in the conventional direct torque control (DTC) for permanent magnet synchronous motor (PMSM) drive, a novel DTC method using space vector pulse width modulation (SVM) is proposed based on analysis of PMSM mathematical model. In this novel DTC system of PMSM , traditional torque hysteresis controller and flux hysteresis controller is respectively substituted by PI controller , traditional switch table is substituted by SVM, and traditional PID used for speed controller is substituted by adaptive PID based on artificial neural network . The simulation model of the DTC system of PMSM is built based on MATLAB, simulation results verify the novel system proposed by this paper has faster dynamic response, lower torque ripple and lower flux ripple than traditional system.

  • Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach

    The application of decision making knowledge based methods is to analyze the system and identify its in-depth diagnosis and fault behavior by simulating the expert knowledge from a similar domain. Rule based systems with predefined conditions have been replaced and or upgraded to expert knowledge based systems and further replaced / upgraded by applying machine learning techniques wherein, association rules, reasoning and decision making processes have been considered similar to expert knowledge in resolution of diagnostics during critical scenarios. The application of condition monitoring technique has been widely applied to analyze the behavior pattern of the system. In this paper, vibration signal analysis is performed to study and extract the behavioral pattern of the bearings. Further, machine learning models such as k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), k-Means have been applied to classify the type of fault. Further, Collaborative Recommendation Approach (CRA) has been applied here to analyze the similarity of all the model results to suggest in advance, the replacement and correction of the deteriorating units and prevent severe system break downs and disruptions.

  • Non-Invasive In-Home Sleep Stage Classification Using a Ballistocardiography Bed Sensor

    Longitudinal monitoring of sleep related parameters can be used for early detection of diseases and also as an indication to physicians for effective adjustment of medication and dosage treatments for people at risk. The correlation between sleep disorders and health conditions such as Alzheimer's and Parkinson's diseases has already been reported in the literature. In this paper, we propose the use of a hydraulic bed sensor for sleep stage classification. Our main motivation of using the bed sensor is to provide a non-invasive, in-home monitoring system, which tracks the changes in health conditions of the subjects over time. Regular polysomnography data from a Sleep Lab have been used as the ground truth, with the focus on three sleep stages, namely, awake, rapid eye movement (REM) and non-REM sleep (NREM). A total of 74 features including heart rate variability (HRV) features, respiratory rate variability (RV) features, and linear frequency cepstral coefficients (LFCC) were extracted from the bed sensor data. Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) classification methods were applied to these features. Our results show accuracy as good as 85% with 0.74 kappa, in the detection of these three sleep stages. These results show promise in the ability of the bed sensor to monitor and track sleep quality and sleep related disorders noninvasively.

  • Algorithm of Shot Detection Based on SVM with Modified Kernel Function

    Improving the precision of shot boundary detection is very important. This paper presents an algorithm for shot boundary detection based on SVM (support vector machine) in compressed domain. It uses the features, such as the type of macroblock, the difference between DC coefficients of two co-located blocks in successive frames and the type of frame, to segment a video into the shots by classifying the frames into three classes, namely, the frames of cut change, gradual change and non-change. In order to further improve the detection accuracy of shot boundary, we modify the kernel function of SVM based on its nature, and some experiments have been done to compare with other kernel functions commonly used. The experimental results show that the classifier with the kernel function of RBF + Gaussian RBF has the better classification performance and achieved higher recall and precision of shot detection.

  • Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines

    Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) was proposed that extends the large-margin idea to feature extraction. It often outperforms traditional methods such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in the SVM, its time complexity is cubic in the number of training points m, and is thus computationally inefficient on massive data sets. In this paper, we propose an (1 + isin)2-approximation algorithm for obtaining the MMDA features by extending the core vector machine. The resultant time complexity is only linear in m, while its space complexity is independent of m. Extensive comparisons with the original MMDA, KPCA, and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy, and is also faster than these kernel-based methods by over an order of magnitude.

  • Detection of Transformer Internal Faults by Using Dynamic Principle Component Analysis

    In this paper; a method is proposed to detect parameter faults in nonlinear systems. The proposed method is called a dynamic principal component analysis approach. In this approach, the detection is based on the manipulation of input and output data without assuming any model for the system. The approach is based on the principal component analysis of the system input-output correlation data on a horizon going a specified number of steps backward. This method is applied to a custom-built transformer in order to detect internal short circuit faults. It is observed through various application examples that the proposed method leads to satisfactory results in the sense of detecting parameter faults in non-linear dynamical systems.

  • A novel fault diagnosis method design and application for civil aircraft system

    For estimating faults of the system with unknown nonlinear term, a novel fault diagnosis method based on nonlinear compensation term and proportional multiple-integral observer is proposed. In this method, the nonlinear compensation term is constructed by support vector machines (SVM), which can reduce the influence of unknown nonlinear part. Proportional multiple-integral observer based nonlinear compensation term can estimate actuator faults when there are sensor faults or output disturbance in the system. The proposed method is applied in civil aircraft system. Simulation experiments are given to demonstrate the efficiency.

  • An effective learning approach for nonlinear system modeling

    Traditional neural networks have found its widespread applications in system identification for a decade, however, several key issues remains unsolved completely in terms of network architecture design and network structure determination. Support vector machine (SVM), a statistical learning approach which performs structural risk minimization, provides a new basis for nonlinear system approximation. In this work, the application of SVMs to nonlinear system identification is described and discussed. Simulation studies demonstrate the effectiveness of this new modeling approach.

  • Real-time emotion classification of tweets

    Despite adding emotions to applications has proven to enhance the user experience, emotion recognition applications are still not widely available nor used. Within this paper, emotion recognition is done on Twitter tweets using six emotion classification algorithms that are compared on precision and timing. The paper shows that precision can be enhanced by 5.02% compared to the current state-of-the-art by improving the features. Furthermore, the presented algorithms work in real-time.



Standards related to Systems Support

Back to Top

No standards are currently tagged "Systems Support"