Showing papers in "ECTI Transactions on Electrical Engineering, Electronics, and Communications in 2022"
TL;DR: In this paper , the expected values of UAV radio-heat contrasts were determined for different weather conditions and wave ranges and their frequency dependence on brightness temperature and composite fiberglass materials were analyzed.
Abstract: This work describes the procedure for determining the expected values of UAV radio-heat contrasts () and discusses its angular dependences, as well as the estimation of UAV detection distances at four points cm and mm ranges (12 GHz, 20 GHz, 34 GHz, and 94 GHz). This paper reveals the pronounced frequency dependence on brightness temperature () and of a fiberglass unmanned aerial vehicle (UAV) made from composite fiberglass materials. The quantified experiments are conducted against a sky background under various weather conditions and wave ranges. The qualitative physical interpretation of these properties and their frequency dependence is proposed, reflecting the coefficient values and radio brightness of the background. The weak influence of weather on the observed UAVs in the X and Ku bands are demonstrated along with the multiple decreasing detection characteristics and advantages of the W band under bad weather conditions (the appearance of rain or thick cloud). This work presents data on the values of UAV contrasts, observed against the background of the sky and the regularities noted could be useful for predicting the effectiveness of the proposed radiometric detection and tracking system.
17 citations
TL;DR: In this article , a multivariate linear regression (MLR) machine learning algorithm is used to predict the maximum power available at the panel, and the voltage corresponds to this maximum power for specific values of irradiance and temperature.
Abstract: Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its conversion efficiency and methodology for tracking the MPP. This paper introduces a new method for operating the PV panel at MPP by implementing the multivariate linear regression (MLR) machine learning algorithm. The MLR machine learning model in this study is trained and tested using the data collected from the PV panel specifications. This MLR algorithm can predict the maximum power available at the panel, and the voltage corresponds to this maximum power for specific values of irradiance and temperature. These predicted values help in the calculation of the duty ratio for the boost converter. The MATLAB/SIMULINK results illustrate that, as time progresses, the PV panel is forced to operate at the MPP predicted by the MLR algorithm, yielding a mean efficiency of more than 96% in the steady-state operation of the PV system, even under variable irradiances and temperatures.
8 citations
TL;DR: In this paper , a 10 kW wireless power transfer (WPT) system was developed for electric vehicle charging in Thailand, where the experimental procedure firstly required the design of block ferrite EE55 cores, and then the transmitter and receiver coils were constructed from homemade Litz wire.
Abstract: The electric vehicle (EV) market is rising despite the COVID-19 pandemic in Thailand and the rest of the world. The Energy Policy and Planning Office, Ministry of Energy, is supporting the development of EV charging stations in Thailand. However, recent research published by Thais on the subject does not involve more than 1.24 kW wireless power transfer (WPT), whereas commercial EVs need at least 3.5 kW charging facilities. This study aims to develop a 10 kW WPT for EV charging in Thailand. The experimental procedure firstly required the design of block ferrite EE55 cores. Secondly, the transmitter and receiver coils were constructed from homemade Litz wire. Thirdly, the prototype magnetic parameters were measured and simulated. A 10 kW high-frequency inverter was then built and tested. The 10 kW prototype IPT system was subsequently simulated, constructed, and characterized. The results revealed that when the prototype IPT system was applied to the resistive tungsten halogen load during the first stage of the research, at 369.4 V DC input voltage and 32.33 A DC input current, the DC output voltage, and currents were 362.4 V and 29.67 A, respectively, while the maximum DC output power and the dc-to-dc efficiency equated to 10.75 kW and 90.00%, respectively.
4 citations
TL;DR: A fault diagnosis method for open circuit (OC) and short circuit (SC) BLDC motor drives using a hybrid classifier with hybrid optimization and teaching-learning-based optimization are introduced.
Abstract: The brushless direct current (BLDC) motor drive is gaining popularity due to its excellent controllability and high efficiency. This paper introduces a fault diagnosis method for open circuit (OC) and short circuit (SC) BLDC motor drives using a hybrid classifier with hybrid optimization. Features such as current, voltage, speed, and torque are considered as the training data. The features are extracted by discrete wavelet transform (DWT) and then employed to train the classifiers to distinguish between fault types and values of response parameters using the support vector machine and Naive Bayes classifier (SVM-NB). To further improve the performance of the system, hybrid chaotic particle swarm optimization (CPSO) algorithms and teaching-learning-based optimization (TLBO) are used. This method is capable of detecting and recognizing the type of faults in the BLDC motor. The developed approach is implemented on the MATLAB/SIMULINK for OC, SC, and no-fault conditions. These hybrid algorithms provide better performance compared to existing approaches with respect to sensitivity, accuracy, and specificity. This improved model achieves about 98.8% accuracy.
4 citations
TL;DR: In this paper , the authors presented an SRM sensitivity analysis focusing on weight of the materials used for stator and rotor for use in electric vehicle application, and the results were discussed, focusing on the fitness of the considered SRM structures for EV application.
Abstract: The switched reluctance motor (SRM), an environmentally friendly machine, is suitable for application in electric vehicles (EV) in recent days. This paper presents an SRM sensitivity analysis focussing on weight of the materials used for stator and rotor for use in electric vehicle application. Two SRM structures are considered, namely normal stator tooth and tapered stator tooth. Thirteen materials widely used in the SRM are considered for these two structures to perform the proposed sensitivity analysis. The major outputs of torque, speed, power, iron loss, efficiency, copper loss, and power factor are gathered through finite element analysis (FEA) simulation. Each of the 13 materials are examined for normal tooth and tapered tooth geometrical structures. The results are discussed, focusing on the fitness of the considered SRM structures for EV application. As weight-sensitive vehicles, the materials used in EVs are compared against the respective motor weights obtained through analysis to recommend the lightest motor for production.
3 citations
TL;DR: In this article , the analysis of a 4×4 PV array configuration under different partial shading conditions (PSCs) is presented, and the performance of various types of 4× 4 PV array configurations under different shading situations are compared and analyzed.
Abstract: Electrical energy usage has drastically increased in recent decades, resulting in significant demand for renewable energy sources, especially solar. With the development of technology, extracting energy from photovoltaic (PV) modules has become easier and more economical. The performance of PV array decreases under an intermittent environment such as partial shading conditions (PSCs), causing fluctuations in PV array power output. This paper presents the analysis of a 4×4 PV array configuration under different PSCs. The power output of PV array depends on factors such as the type of configuration, size of array, and shading patterns. The performance of various types of 4×4 PV array configurations under different shading situations are compared and analyzed in this study, and the results presented.
3 citations
TL;DR: In this article , a two-axis solar tracker is proposed to find the best tilt and surface azimuth angles of PV panels of an all-electric ship (AES) for the reduction of fuel consumption and carbon emissions.
Abstract: The all-electric ship (AES) offers new hope for the reduction of fuel consumption and carbon emissions. To fully exploit the photovoltaics (PVs) installed on the AES, the two-axis solar tracker is proposed to find the best tilt and surface azimuth angles of PV panels. The tilt angle is computed by particle swarm optimization (PSO) regarding the time and ship location. The surface azimuth angle is adjusted according to the hemisphere. The dynamic performance of the proposed solar tracker is evaluated using MATLAB/Simulink simulation. According to the simulation results, the proposed solar tracker can obtain maximum energy all day, while the voyage time, fuel consumption, and carbon emissions are significantly reduced. Moreover, the proposed solar tracker can effectively operate under communication delay caused by the global positioning system (GPS).
2 citations
TL;DR: In this paper , a fault-tolerant multilevel inverter topology based on a cross-connected source-based MLIs (CCS-MLI) is proposed.
Abstract: Multilevel inverters (MLIs) are very popular in renewable energy applications and other DC to AC conversion systems due to their reliability, reduced voltage stress, low total harmonic distortion (THD), reduced filter size, low electromagnetic interference, etc. Consequently, the photovoltaic (PV) generation systems, mainly installed in remote areas, require highly reliable systems. The high failure rate of sources and power semiconductor devices results in very low reliability for inverters used in PV generation systems. The aim of this study is to develop a five-level MLI topology with fault-tolerant (FT) characteristics. Therefore, a highly resilient fault-tolerance topology, based on a cross-connected source-based MLIs (CCS-MLI) structure, is proposed in this paper. The developed CCS-MLI topology can tolerate open switch faults in any single switch failure. The proposed system and results developed in a MATLAB/Simulink environment are discussed under normal and faulty states. The simulation results are validated experimentally. Finally, the quantitative and qualitative superiority of the proposed CCS-MLI is demonstrated through the comparative analysis of other recent topologies.
2 citations
TL;DR: In this article , the effect of magnetic energy based on slot-pole combinations to evaluate the performance of a brushless direct current (BLDC) motor for agro electric vehicle (agro-EV) applications is analyzed.
Abstract: This paper analyzes the performance of a brushless direct current (BLDC) motor for agro electric vehicle (agro-EV) applications. Agro-EV technology is being developed in response to increasing environmental pollution. Various types of electric motors are in agro-EV, one of which is the BLDC. With its good capabilities, it has been chosen for further exploration in this research. On the other hand, some issues limit the usage of the conventional BLDC motor in heavy applications, such as low torque performance caused by weak magnetic energy. Therefore, this research aims to analyze the effect of magnetic energy based on slot-pole combinations to evaluate the BLDC motor's performance. Three BLDC models with different slot-pole numbers are designed and simulated using a fixed structure size, permanent magnet volume, and magnetomotive force (MMF). Finite element method (FEM) software known as Altair Flux 2D is used to compute the cogging torque, back-electromotive force (BEMF), magnetic flux density, and the torque produced. As a result, an 18/20 slot-pole was chosen for its high torque (105 Nm) and BEMF (35.9 V). In conclusion, this research simulation presents guidelines and an overview regarding the effect of slot-pole numbers on the performance of the BLDC motor for agro-EV applications.
2 citations
TL;DR: The deep neural network (DNN) has been proposed for end-to-end performance and demonstrated superiority over the conventional LMMSE for channel estimation and signal detection in wireless communications with complex channel distortion and interference.
Abstract: Orthogonal frequency division multiplexing (OFDM) plays an important role in wireless communication due to its high transmission rate. Information is conveyed across spatial and temporal dimensions through the space-time shift keying (STSK) technique which is basically used to handle multiplexing diversity and gains. On the other hand, index modulation integrated OFDM not only communicates information through conventional signal constellations as in classical OFDM, but also through indexes of the subcarriers. In index modulation, the subcarriers are transmitted over a particular index and can be implemented effectively. The active indices are selected and further information bits transmitted. In this paper, to handle such limitations, the deep neural network (DNN) has been proposed for end-to-end performance. Under the noisy and faded channel scenario, channel state information must be acquired to recover the transmitted signal correctly. To evaluate the channel distortion level, a deep learning model is trained offline from simulated data and then applied to online data to estimate and recover the channel state as well as the transmitted signal, respectively, in comparison to the traditional least minimum mean square error (LMMSE) channel estimation technique. The analysis results demonstrate superiority over the conventional LMMSE for channel estimation and signal detection in wireless communications with complex channel distortion and interference. The mean square error (MSE) is evaluated for carrying out performance information in each subcarrier block and to reduce the detector error rate.
2 citations
TL;DR: In this article , a modified space vector pulse width modulation (SVPWM) technique for a five-level inverter that provides complete control over the multiple space vector voltages of a six-phase inverter is presented.
Abstract: This paper presents a modified space-vector pulse width modulation (SVPWM) technique for a five-level inverter that provides complete control over the multiple space-vector voltages of a six-phase inverter. This technique involves splitting six phases into two three-phase five-level inverters connected in parallel. A six-phase induction motor (SPIM) drive with a distributed neutral is considered as the load. This paper also presents a comparative analysis between the proposed and conventional SVPWM inverter-fed SPIM drives. To investigate the analytical developments and voltage limits, MATLAB/Simulink environment was used in this study. A prototype was developed in the laboratory for analyzing the harmonic components of the phase voltages and currents. The efficacy of the proposed technique was validated by means of comprehensive experiments, and the results discussed herein.
TL;DR: This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric Authentication techniques using EEG.
Abstract: An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity in the brain. EEG signals are effective for biometric authentication and robust against malware attacks and any kind of fraud activities due to the uniqueness of the signals. Significant progress in research on EEG-based authentication has been achieved in the last few years, with machine learning being extensively used for classifying EEG signals. However, to the best of our knowledge, there has been no investigation into the overall progress made in such research. In this paper, the literature on the various factors involved in state-of-the-art biometric authentication systems is reviewed. We provide a thorough comparison of different machine learning biometric authentication techniques. The comparison criteria include the research objectives, machine learning algorithms, computational complexity, source of brainwaves, feature extraction methods, number of channels, and so on. Alongside the discussion of existing works, directions for future research are suggested to improve authentication accuracy. This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric authentication techniques using EEG.
TL;DR: A speech feature-based correlation (SFC) algorithm and a speech recognition framework are developed, combining specific speech features and performance correlation to monitor real-time radio broadcasting and recognize specific speech based on human samples.
Abstract: The analysis and classification of audio signals are becoming increasingly important, especially in the age of communication and dissemination of information through radio broadcasting systems. It is therefore essential that systems and platforms are available to monitor the spread of fake or fraudulent news. A speech feature-based correlation (SFC) algorithm and a speech recognition framework are developed in this study, combining specific speech features and performance correlation to monitor real-time radio broadcasting and recognize specific speech based on human samples. The speech features include the Mel frequency cepstral coefficient, gammatone cepstral coefficient, spectral entropy, and pitch. The results illustrate the advantages and disadvantages of each feature applied to the various speech sound groups. Furthermore, each feature combined with the design of SFC further enhances system performance and increases accuracy.
TL;DR: A magnetic recording system with associated sectors, constructed using spatially coupled low-density parity-check (SC-LDPC) codes, shows that the associated sectors achieve significant performance gains compared to the traditional non-associated sectors.
Abstract: In traditional magnetic recording systems, non-associated sectors are mainly adopted, whereby two consecutive sectors are decoded independently by the low-density parity-check (LDPC) codes. In this paper, we propose a magnetic recording system with associated sectors, constructed using spatially coupled low-density parity-check (SC-LDPC) codes. If the SC-LDPC decoder cannot correct the erroneous bits in the current sector, it can request information stored in previous sectors to improve decoding performance. Moreover, we modify protograph-based extrinsic information transfer (P-EXIT) charts to examine the theoretical performance of SC-LDPC codes applied to both non-associated and associated sectors. Our theoretical results show that the associated sectors achieve significant performance gains compared to the traditional non-associated sectors.
TL;DR: In this paper , a programmable logic controller (PLC) along with supervisory control and data acquisition (SCADA) is used for automatic control and monitoring for AC fan evaporator and compressor speeds.
Abstract: The promotion of energy-saving air conditioners (ACs) continues to increase. Therefore, this research proposes the implementation of automatic control and monitoring for AC fan evaporator and compressor speeds using a programmable logic controller (PLC) along with supervisory control and data acquisition (SCADA). The proposed system uses a room-temperature sensor, connected to the PLC and programmed with a ladder diagram. It is connected to a SCADA installed computer for monitoring and data logging. The testing involves three methods: conventional, manual, and automatic PLC-based. Over a four-week period, the conventional method consumed 193.6 kW⋅h, 198.7 kW⋅h, and 206.6 kW⋅h in energy, while the manual method consumed 160.5 kW⋅h, 160.1 kW⋅h, and 161.9 kW⋅h, and the automatic method 150.8 kW⋅h, 152.6 kW⋅h, and 154.8 kW⋅h using the trapezoidal composite rule, Simpson's composite rule, and ordinary methods, respectively. The main research contribution is the provision of an energy-saving system for air conditioners over a long duration using PLC. The PLC-based automatic-to-manual energy savings equate to 6.0%, 5.8%, and 4.4%; whereas 22.0%, 24.0%, and 25.0% for the PLC-based automatic-to-conventional method. Therefore, the PLC-based automatic control is deemed appropriate for electrical energy-saving.
TL;DR: In this article , the optimal placement of phasor measurement units (OPP) using nonlinear programming (NLP) is proposed to enhance the performance of smart grid (SG) systems.
Abstract: This paper presents an efficient observation concerning the enhancement of smart grid (SG) based on the optimal placement of phasor measurement units (OPP) using nonlinear programming (NLP). The proposed algorithm tries to achieve two objectives: (i) to ascertain the minimum number of phasor measurement units (PMUs) and (ii) to increase the redundancy of the SG at all the buses. Synchronized current and voltage phasors are obtained to enhance the accuracy of the state estimation results—a minimum number of PMUs results in a lack of communication facilities at the substation. PMU losses will lead to unobservable buses at the SG. Therefore, PMU losses and communication constraints should be considered during the design process. Limited channel capacity, conventional measurement, and zero-injection bus measurements are also included in the proposed PMU formulation. The proposed algorithm is examined on IEEE~14-, 30-, 57-, 118-, and 300-bus test systems in MATLAB to verify its effectiveness. Furthermore, the results are compared with the simplex linear programming and mixed linear programming methods to prove the efficacy of the presented algorithm. The output thus obtained reveals that the NLP algorithm obtains approximately the same PMUs as other methods.
TL;DR: This research work proposes a method to rigorously model a 3D human head from the informative data in magnetic resonance imaging (MRI), based on each slice of 53 MRI resized to a 64 × 64 grayscale image to enable a practical simulation.
Abstract: This research work proposes a method to rigorously model a 3D human head from the informative data in magnetic resonance imaging (MRI). The approach is based on each slice of 53 MRI resized to a 64 × 64 grayscale image to enable a practical simulation. The optimized unit cell of 5.6 × 5.6 × 5.6 mm3 is identified as a particular type of tissue. It corresponds to the average size of an actual human brain. The material properties are assigned to various tissues of the entire structure of the human head. The computational model of this will then be used as a virtual object to study the specific absorption rate (SAR) with electromagnetic radiation (EMR) at 2.6 GHz as a 5G mid-band frequency. A commonly known finite-different time-domain (FDTD) method is used as a tool in the SAR simulation. The key results show that a handset with a power of less than 0.8 W (Watts), operating at a handset to head separation distance of 1.12 cm, will meet the FCC SAR 1g limit of 1.6 W/kg.
TL;DR: In this paper , an energy transfer system for a photovoltaic module with solar-powered panels, batteries, and a grid-tie inverter that uses series-connected bidirectional resonant converters was presented.
Abstract: This paper presents an energy transfer system for a photovoltaic module with solar-powered panels, batteries, and a grid-tie inverter that uses series-connected bidirectional resonant converters. The purpose of this study is to investigate the asymmetrical duty cycle control and frequency control techniques for increasing and controlling the input voltage of the grid-tie inverter. Prior to the experiment, the performance of the bidirectional resonant converters was evaluated using a simulation program. By adjusting the asymmetrical duty cycle by 50%, the input voltage of the grid inverter was found to be 48.2 V. However, by adjusting the asymmetrical duty cycle by 10%, the input voltage of the grid inverter was 150.4 V. Furthermore, when turning on the switch in the zero-voltage switching (ZVS) mode, the converter circuit was controlled and operated within an appropriate frequency range. The results revealed no switching losses when the converters were turned on. As a result, the bidirectional resonant converters were able to properly transfer energy and regulate the input voltage of the grid inverter.
TL;DR: This paper analytically analyzes the proposed novel unidirectional interval intersection method for mitigating the uncertainty in the interval width and simulated under three different delay models: uniform, normal, and truncated exponential.
Abstract: This paper proposes a novel interval intersection-based protocol for time coordination in wireless sensor and IoT networks. The common notion of time amid the nodes in a distributed environment can be achieved through the message exchange process, which experiences random delay (send, access, propagation, and reception), thus making the time coordination process difficult. Several researchers have proposed algorithms to handle the error in estimation using various methods. This paper analytically analyzes the proposed novel unidirectional interval intersection method for mitigating the uncertainty in the interval width. The offset and slope estimation errors are then reduced under different conditions to verify the effectiveness of the proposed coordination algorithm. The model is simulated under three different delay models: uniform, normal, and truncated exponential. Their performance is then compared in terms of coordination efficiency.
TL;DR: In this article , a three-leg voltage source inverter fed by a brushless DC motor was implemented to reduce spike and ripple on the voltage, current, stator flux, and electromechanical torque.
Abstract: This paper presents the implementation of a three-leg voltage source inverter fed by a brushless DC motor. The open-loop speed control with direct torque is employed in this motor. The rotation of the motor is controlled by using an optimal voltage vector which is related to the calculation of stator flux and electromechanical torque. The experimental results show that this system has greater capability to reduce spike and ripple on the voltage, current, stator flux, and electromechanical torque than the conventional speed controller which feeds the voltage vector from the hall-effect sensors.
TL;DR: This paper presents a self-tuning fuzzy logic speed controller (FLSC) with model reference adaptive control (MRAC) for an induction motor (IM) drive system that utilizes seven simplified rules of the 5 × 5 matrix membership functions to minimize the computational burden and memory space limitations.
Abstract: This paper presents a self-tuning fuzzy logic speed controller (FLSC) with model reference adaptive control (MRAC) for an induction motor (IM) drive system. The MRAC is examined by output scaling the factor tuner for optimum motor speed performance. A detailed investigation is carried out on the scaling factor control of the input change error and main FLSC output increment. This proposed method utilizes seven simplified rules of the 5 × 5 matrix membership functions to minimize the computational burden and memory space limitations. All simulation work is conducted using Simulink and Fuzzy Tools in the MATLAB software and the experimental testing with the aid of a digital signal controller board, dSPACE DS1103. Based on the results, the output scaling factor makes a more significant impact on the performance effect compared to the input error scaling factor. The input change error and output SF also exhibit similar behavior, indicating that a large range of UoD tuners works well in terms of capability load rejection while a small range of UoD tuners performs well in terms of rise time. The analysis includes no-load and load tests to ascertain the overshoot percentage, rise time, and settling time for transient and steady-state conditions.
TL;DR: In this article , a prosumer case study based on the non-cooperative day-ahead market is used to compare two well-known auction-based and game-based energy clearing methods.
Abstract: In general, the clearing method in the local energy market is introduced based on two well-known methods: auction-based and game theory. However, both methods focus on different aspects; the auction-based method is based on economic equilibrium, whereas game theory is based on the concept of maximum profit. Therefore, to clarify the difference, the processes and algorithms of both methods are discussed and compared in this paper. In this study, the prosumer case study based on the non-cooperative day-ahead market is used to compare both methods. The prosumer is a good case because, as the lowest unit in the local market, it can apply to either seller or buyer. According to the case study, the comparative results focus on the difference between the local price and retail price, and the allocated energy quantity. The findings from the comparative results will advise the market operator on the most appropriate clearing method and market player for the bidding strategy design.
TL;DR: Social spider optimization (SSO), a new swarm algorithm, is employed to concurrently reconfigure and find the best network and it is revealed that SSO is a strategy worth investigating for tackling the network reconfiguration problem.
Abstract: The goal of this paper is to offer a new strategy for solving the network reconfiguration problem with the aim of decreasing real power loss and enhancing the voltage profile in the distribution system. Social spider optimization (SSO), a new swarm algorithm, is employed to concurrently reconfigure and find the best network. The proposed method was tested on 30-bus mesh and 33-bus radial distribution systems at fixed load levels. To show the performance and efficacy of the suggested method, it was compared to optimization methodology, such as the genetic algorithm, harmony search algorithm, Kruskal's maximal spanning tree, discrete evolutionary programming, and cuckoo search algorithm. The findings reveal that SSO is a strategy worth investigating for tackling the network reconfiguration problem.
TL;DR: In this article , the performance of the ML and APFE algorithm is analyzed for CFO estimation in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system.
Abstract: Carrier frequency offset (CFO) estimation in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is investigated in this study. MIMO-OFDM is very sensitive to CFOs due to oscillator frequency mismatch and/or Doppler shift. Inaccurate CFO estimation results in intercarrier interference (ICI) through the loss of orthogonality among subcarriers. In this paper, the performance of the ML and APFE algorithm is analyzed for CFO estimation. ML becomes extremely complex due to the multidimensional exhaustive search issue, which is the basic concern in ML estimation. However, making use of the iterative low complex APFE method, here this multidimensional search is replaced with a sequence of mono-dimensional searches. This results in an estimation algorithm of reasonable complexity which is suitable for practical applications. In addition, ML accuracy is compared with the Cramer-Rao bound (CRB).
TL;DR: It is demonstrated that, when BD precoding greatly attenuates the desired user signals, user grouping can help improve minimum user throughputs even though BD precode can support all users as a single group.
Abstract: This paper investigates efficient user grouping methods for multi-user multi-input multi-output (MU-MIMO) visible light communication (VLC) systems. Block diagonalization (BD) precoding is considered for interference avoidance. In addition, time division multiplexing (TDM) is applied to perform user grouping when the number of users exceeds the limit of BD precoding based on the number of light emitting diode (LED) transmitters and the total number of users' photodiodes (PDs). User grouping methods are proposed based on pairwise interference considerations among users in the same group. The proposed methods can be implemented through integer linear programming (ILP), which requires less computation than exhaustive search. The numerical results on the average minimum user throughputs over random scenarios indicate that the proposed hybrid method can significantly outperform random user grouping and performs reasonably well compared to exhaustive search. Finally, this study demonstrates that, when BD precoding greatly attenuates the desired user signals, user grouping can help improve minimum user throughputs even though BD precoding can support all users as a single group.
TL;DR: In this article, an online post-fault transient stability assessment method using synchrophasor or PMU measurements is proposed using a regression model trained offline to predict the normalized stability margins.
Abstract: An online post-fault transient stability assessment method is proposed in this study using synchrophasor or PMU measurements. Initially, a post-fault multimachine system is converted into a suitable one machine infinite bus (OMIB) system using the single machine equivalent (SIME) method. Thus, the - trajectory obtained through the OMIB system enabled a normalized transient stability index to be calculated offline. By using synchrophasor measurements before and during the fault as inputs, the regression model can be trained offline to predict the normalized stability margins. Following a fault, the synchrophasor measurements are used as input to this trained model for online stability margin prediction. If the predicted margin is negative, then the post-fault power system is indicated to be unstable. Alternatively, positive values for the predicted margin identify the system as stable. The proposed assessment method is verified using the New England (NE) 39 bus test system. The results obtained are then compared with offline simulations.
TL;DR: In this article , a control structure for a non-linear pitch control system using an advanced neuro-fuzzy tuned PID (NF-PID) controller was developed on the MATLAB Simulink platform and the obtained simulation results satisfy the requirements of constant output power even if the wind speed input changes abruptly.
Abstract: Modern power systems comprise a variety of generating systems, including conventional thermal power stations and advanced renewable generating sources, one contender being a wind energy conversion system (WECS). Blade pitch control is an important part of the highly non-linear WECS. Many control strategies have been proposed by researchers around the globe. Current research work focuses on developing a control structure for a non-linear pitch control system using an advanced neuro-fuzzy tuned PID (NF-PID) controller. This approach utilizes the simplicity of a PID controller and the power of a soft computing technique like neuro-fuzzy to handle non-linearity. The model in this study is developed on the MATLAB Simulink platform and the obtained simulation results satisfy the requirements of constant output power even if the wind speed input changes abruptly.
TL;DR: In this article , the impact of single-event upsets on the resistance of resistive random access memory (RRAM) was investigated by means of a double exponential current pulse, and the performance of the device was compared in terms of resistance before and after irradiation.
Abstract: Resistive random access memory (RRAM) is a promising candidate for industry and academia from the research and development perspective. The resistance of RRAM depends on the geometrical dimensions, growth, and rupture of the conductive filament. In this work, the geometrical dimensions such as the length and width of the filament are varied to analyze the resistance. Moreover, the RRAM can be used in aerospace applications. Therefore, the impact of a single-event upset on resistance of RRAM is investigated by means of a double exponential current pulse. The performance of the device is compared in terms of resistance before and after irradiation. A decrease in its original resistance has been observed after radiation.
TL;DR: The proposed system operates by responding to the two flow entries for each request and removing packet-in messages from some unused multicast traffic, thereby reducing the load and traffic in the network as well as packet loss.
Abstract: In OpenFlow networks, several packet-in traffic messages are sent to the controller to request routes from any switch on the network. Executing these packet-in messages and replying to control messages may interfere with the controller performance. Therefore, a mechanism for lightening the load on the controller by reducing packet-in traffic between the controller and the switch is proposed in this research. The proposed system operates by responding to the two flow entries for each request and removing packet-in messages from some unused multicast traffic. The proposed system can thus not only avoid a third packet-in message but also some multicast packet-in traffic, thereby reducing the load and traffic in the network as well as packet loss. According to the evaluation results, the proposed system can improve network performance by significantly reducing packet-in overhead.
TL;DR: Inference results obtained indicate that the queue with predictive parameters employing beta distribution, even when dealing with a loss system queue, involves less transition time and a greater load than a queue with coarse parameters; hence, preventing a long tail in the queue which is the cause of packet loss.
Abstract: In this paper, a parametric prediction model is proposed for a queuing system driven by the Markov process. The aim of the model is to minimize the packet loss caused by time dependency characterized by the queue tail being too long, resulting in a time-out during the transfer of a large dataset. The proposed technique requires the key parameters influencing the queue content to be determined prior to its occupation regardless of the server capacity definition, using Bayesian inference. The subsequent time elapsing between the arrival and departure of a packet in the system is given, as well as the system load. This queue content planning is considered as the Markov birth-death chain; a type of discretization that characterizes almost all queuing systems, leading to an exponential queue, and captured herein using beta distribution. The inference results obtained using this exponential queue indicate that the queue with predictive parameters employing beta distribution, even when dealing with a loss system queue, involves less transition time and a greater load than a queue with coarse parameters; hence, preventing a long tail in the queue which is the cause of packet loss.