This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the li...
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This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.
This paper proposes the Bacterial Foraging Optimization (BFO)-based tuning of controllers for a pancake DC torque motor in the framework of a Diesel engine exhaust gas recirculation valve as a representative automotiv...
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This paper proposes the Bacterial Foraging Optimization (BFO)-based tuning of controllers for a pancake DC torque motor in the framework of a Diesel engine exhaust gas recirculation valve as a representative automotive torque motor actuator. The validation of the position of bacteria only if the control system response is in a valid range is inserted in the BFO algorithm. PID and sliding mode controllers are optimally tuned by the BFO algorithm focused on solving an optimization problem that minimizes an objective function expressed as the weighted sum of overshoot plus the integral of squared error. The parameters of these two controllers belong to the vector variables of the objective function. A case study that deals with the shaft angle control of an automotive torque motor actuator is included to validate our approach by simulation results. The comparison of control system performance is carried out.
This paper discusses the implementation of an efficient anti-collision algorithm for RFID systems. Conventional TDMA methods do not show a high efficiency and speediness in collision detection and do not take into acc...
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This paper discusses the implementation of an efficient anti-collision algorithm for RFID systems. Conventional TDMA methods do not show a high efficiency and speediness in collision detection and do not take into account the spatial characteristics of tags distribution. By combining the TDMA (Time Division Multiple Access) and SDMA (Space Division Multiple Access) algorithms, the proposed approach takes into account the characteristics of the tag position in the reading area of the reader. Knowing that the SDMA method is based on the principle of Digital Beam Forming (DBF), we used the FFT and IFFT blocks to form the different beams and the OFDM technique to meet the orthogonality condition. We then divided the space into two subsets of beams and applied the TDMA method to detect any collision. The developed RFID anti-collision SDMA system has been coded by VHDL and simulated using the ISE software for FPGA implementation.
This paper suggests a model-free tuning solution for a sliding mode control system (SMCS) structure dedicated to servo systems. The new SMCS structure is viewed in the framework of reference trajectory tracking using ...
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This paper suggests a model-free tuning solution for a sliding mode control system (SMCS) structure dedicated to servo systems. The new SMCS structure is viewed in the framework of reference trajectory tracking using a first-order nonlinear dynamic system as a local approximation of the process model. The sliding mode control signal augments the control signal specific to a model-free PI control system (CS) structure in order to compensate for the estimation errors which affect the systematic design and performance. The derivatives in the local approximation of the process model are estimated numerically using a Savitzky-Golay filter to carry out both differentiation and smoothing. A simple design approach is proposed for the SMCS structure. The real-time experimental results concerning the speed control of a laboratory nonlinear DC servo system prove the performance improvement of the SMCS structure against a model-free PI CS structure.
This paper suggests new data-driven Model-Free Control (MFC) and Model-Free Adaptive Control (MFAC) algorithms for Multi Input-Multi Output (MIMO) twin rotor aerodynamic systems. The discrete-time formulation of the a...
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This paper suggests new data-driven Model-Free Control (MFC) and Model-Free Adaptive Control (MFAC) algorithms for Multi Input-Multi Output (MIMO) twin rotor aerodynamic systems. The discrete-time formulation of the algorithms is given in the framework of a MIMO control system structure with azimuth and pitch position control loops. The MFC and MFAC algorithms are validated by a set of experimental results on representative laboratory equipment. The performance comparison of the MFC- and MFAC-based MIMO control systems and azimuth and pitch position control is carried out considering three experimental scenarios.
The paper investigates dual-link fault localization in all-optical ring networks using optical probes (called monitoring bursts). By defining a single monitoring node (MN) in the ring that initiates and terminates a s...
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The paper investigates dual-link fault localization in all-optical ring networks using optical probes (called monitoring bursts). By defining a single monitoring node (MN) in the ring that initiates and terminates a set of monitoring trails (m-trails), the MN can localize any dual-link fault by inspecting the on-off status of the launched optical bursts. We investigate relevant problems in the proposed fault monitoring approach, including m-trail allocation, burst launching time scheduling, and node switch fabric configuration, where constructions are developed to derive optimal solutions and are further examined in numerical experiments. 2(|Ej| - 1) m-trails and at most (2δ + L)(|E| - 1) + δ(|E| - 2) of monitoring delay are required for dual-link fault localization from a single MN in a ring with|E| links, where δ is the burst propagation delay along a unidirectional link and L is the burst length.
The Invasive Weed Optimization (IWO) is an effective evolutionary and recently developed method. Due to its better performance in comparison to other well-known optimization methods, IWO has been chosen to solve many ...
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The Invasive Weed Optimization (IWO) is an effective evolutionary and recently developed method. Due to its better performance in comparison to other well-known optimization methods, IWO has been chosen to solve many complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO is applied to optimize the geometry of a realistic log-periodic dipole array (LPDA) that operates in the frequency range 800-3300 MHz and therefore is suitable for signal reception from several RF services. The optimization is applied under specific requirements, concerning the standing wave ratio, the forward gain, the gain flatness and the side lobe level, over a wide frequency range. The optimization variables are the lengths and the radii of the dipoles, the distances between them, and the characteristic impedance of the transmission line that connects the dipoles. The optimized LPDA seems to be superior compared to the antenna derived from the practical design procedure.
This paper describes, a graphical user interface (GUI) for synthetic simulation of maternal-foetal ECG mixtures. The GUI is directly linked to the previously introduced fecgsyn ECG model, which was used for producing ...
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This paper describes, a graphical user interface (GUI) for synthetic simulation of maternal-foetal ECG mixtures. The GUI is directly linked to the previously introduced fecgsyn ECG model, which was used for producing a subset of data of the Physionet/Computing in Cardiology Challenge 2013. fecgsyngui serves as a tool that facilitates the use of the broad capabilities of fecgsyn.
Introduction: The electrocardiogram (ECG) allows for interpretation of the electrical activity of the heart. The information which can be derived from the foetal ECG (FECG) goes beyond heart rate and heart rate variab...
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Introduction: The electrocardiogram (ECG) allows for interpretation of the electrical activity of the heart. The information which can be derived from the foetal ECG (FECG) goes beyond heart rate and heart rate variability. However morphological analysis of the FECG waveform is usually not performed in clinical practice. Methods: A Bayesian Filtering Framework based on an Extended Kalman Filter (EKF) for extracting the FECG from a single abdominal channel is described using a training database of 20, one minute maternal-foetal mixtures and evaluated on 200, one minute mixtures. (Data was generated using the simulator, fecgsyn, used to generate a subset of the signals of the Physionet Challenge 2013.) A single pass of the EKF (EKFS) was performed to cancel out the maternal ECG (MECG) in order to build an average FECG morphology. A dual EKF (EKFD, i.e. where both the MECG and FECG cycle morphology were modelled) was then applied to separate the three sources present in the signal mixture (noise, MECG and FECG). A normalised root mean square error and absolute QT error after EKFS and EKFD were calculated. Results: An SNR improvement of 1.97 dB after EKFS and 14.14 dB after EKFD on the test set were achieved. Median absolute error on QT measurement was 17.0 ms for the EKFS and 4.0 ms for the EKFD. Conclusion: This work is a proof of concept that the EKFD allows accurate beat to beat extraction of the FECG morphology from abdominal recordings.
Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worse...
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Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters; sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.
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