Search

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids. [ABSTRACT FROM AUTHOR]

A new equivalent dipole model hybrid with artificial neural network (ANN) is proposed in this paper for electromagnetic interference (EMI) estimation. Equivalent dipole method, based on the free-space Green’s function, is usually used to model unknown EMI sources on printed circuit boards. For high-speed and dense circuits, there may be multi-reflection and/or diffraction between the EMI source and its nearby components. Traditional dipole model usually omits such effects and leads to an inaccurate result in some cases. In our proposed method, the Green’s function of dipole is taken as input and the radiated EMI field is taken as the output of ANN. We use the powerful mapping ability of ANN to modify the matrix–vector multiplication between free-space Green’s function and dipole moments in the traditional dipole model, so that a new mapping between equivalent dipoles and their radiated fields is established. The near field of the EMI source is obtained by planar scanning, and is used for ANN training. After training, the ANN is used to predict the EMI field at the region of interest. Both numerical example and measurement example are given to show the effectiveness of the proposed ANN method. This paper provides a novel source reconstruction solution for the EMI problems. [ABSTRACT FROM AUTHOR]

This paper presents a sensitivity analysis of similar days (SD) parameters to increase the accuracy of artificial neural network (ANN) and SD-based short-term price forecasting. Work presented in this paper is an extended version of previous works done by the authors to integrate ANN and SD method for predicting electricity price. The focus here is on sensitivity analysis of SD parameters while keeping the parameters same for ANN to forecast hourly electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) (regional transmission organization in northeast America) electricity market. Several cases are simulated by choosing (a) two, (b) three, (c) four, and (d) five SD parameters to calculate the norm. In addition, sensitivity analysis has been carried out by changing the time framework of SD (d = 15,30, 45, 60) and the number of selected similar price days (N = 5, 10). From sensitivity analysis, it is identified that the optimized mean absolute percentage error (MAPE) is obtained using case-c with d = 30 and N = 10. MAPE of reasonably small value, along with forecast mean square error and mean absolute error of around 2$/MWh and 1$/MWh, is obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by the developed ANN model based on the optimized case are accurate and efficient. [ABSTRACT FROM AUTHOR]

Multi-robot navigation is a challenging task, especially for many robots, since individual gains may more often than not adversely affect the global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace whereas the motion of every individual robot is deduced by a novel Particle Swarm Optimization (PSO) tuned Feed Forward Neural Network (FFNN). Motion coordination among the robots is implemented using a cooperative coordination algorithm that identifies critical robots and maintains cooperation count while actuating deviation in select robots. The contribution of this paper is twofold; firstly in hybridizing the Artificial Neural Network(ANN) by employing PSO, an evolutionary algorithm, to find optimal values of deviation for every critical robot using velocity and acceleration constraints, secondly ensuing the convergence of the PSO by carrying first and second order stability analysis. Experiments have been carried out to evaluate and validate the efficacy of the proposed coordination schemes by changing the number of robots under hundred different scenarios each, and the founded results demonstrate the efficacy of the proposed schemes. [ABSTRACT FROM AUTHOR]

Current transformers (CTs) are extensively used for the measurement of electrical energy, and hence they play a crucial role in the revenue generation for power distribution utilities. The novelty of this paper is that it presents artificial neural network (ANN) application for the ratio and phase angle error corrections of CTs in the presence of harmonic distortions. In this paper, the behaviors of silicon iron alloy and nanocrystalline alloy CTs are examined by the means of a number of experimental tests under the sinusoidal and distorted waveform conditions. The relation between the nature of CTs secondary current waveforms (CTSwf) with their metrological properties, connected burdens, and nature of load currents (linear or nonlinear) is established. The training data are obtained by the nature of CTSwf in terms of amplitude errors, time shifts, and deviations caused by different harmonic contents between input and output quantities. The ANN is trained for various operating conditions. The implementation of the error correction system and data acquisition is done using dSPACE 1104 software. The experimental results illustrate that the proposed technique can be used for error compensation in case of sinusoidal and the distorted waveform composed of fundamental and many harmonics. [ABSTRACT FROM AUTHOR]

Starting from measurements performed at 1140 MHz in an urban environment (Rio de Janeiro, Brazil), the signal strength measured is compared with the usual predictions calculated using the methods of International Telecommunication Union-Radiocommunication (ITU-R) Rec. 526-11/Cascade Knife Edge, and ITU-R Rec. 526-12/Delta-Bullington. The first model presented better adjustment to the data and, by using artificial neural networks (ANNs), this paper deals with the signal coverage prediction in a mobile channel calculated by the ITU-R model, which is used to train the ANN in order to reduce the average deviations. This hybrid application (ITU-R/ANN) showed efficiency, resulting in higher accuracy for the signal strength prediction, and an average improvement of 8 dB was achieved. [ABSTRACT FROM PUBLISHER]

The demand and relevance of efficient analog fault diagnosis methods for modern RF and microwave-integrated circuits increase with the growing need and complexity of analog and mixed-signal circuitry. The well-established digital fault diagnosis methods are insufficient for analog circuitry due to the intrinsic complexity in analog faults and their corresponding identification process. In this paper, we present an artificial neural network (ANN) modeling approach to efficiently emulate the injection of analog faults in RF circuits. The resulting metamodel is used for fault identification by applying an optimization-based process using a constrained parameter extraction formulation. A generalized neural modeling formulation to include auxiliary measurements in the circuit is proposed. This generalized formulation significantly increases the uniqueness of the faults identification process. The proposed methodology is illustrated by two faulty analog circuits: a CMOS RF voltage amplifier and a reconfigurable bandpass microstrip filter. [ABSTRACT FROM AUTHOR]

As microprocessor design scales to the 10-nm technology and beyond, traditional pre- and post-silicon validation techniques are unsuitable to get a full system functional coverage. Physical complexity and extreme technology process variations severely limits the effectiveness and reliability of presilicon validation techniques. This scenario imposes the need of sophisticated post-silicon validation approaches to consider complex electromagnetic phenomena and large manufacturing fluctuations observed in actual physical platforms. One of the major challenges in electrical validation of high-speed input/output (HSIO) links in modern computer platforms lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel undesired effects induced by the channels. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive laboratory measurements. An alternative is to use machine learning techniques to model the PHY, and then perform equalization using the resultant surrogate model. In this paper, a metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings. We use several design of experiments techniques to find a neural model capable of approximating the real system behavior without requiring a large amount of actual measurements. We evaluate the models performance by comparing with measured responses on a real server HSIO link. [ABSTRACT FROM AUTHOR]

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a spin-orbit torque driven skyrmionic device that acts as a synaptic element in a hardware artificial neural network (ANN) and compared its performance with similar domain wall-based device in the presence of defects in the material that can pin the domain wall or the skyrmion. Next, we have simulated a feedforward ANN with either a domain wall device or a skyrmionic device as the synaptic element and solved a standard digit recognition problem with it using the back-propagation algorithm to show that a skyrmionic synapse-based ANN consumes two orders of magnitude lower energy than a domain wall synapse-based ANN. [ABSTRACT FROM AUTHOR]

The calculation of ac loss is an essential part in the thermal stability estimation of the high-temperature superconductor (HTS) superconducting magnetic energy storage system (SMES). In this paper, we propose an artificial neural network (ANN) prediction model to calculate the ac loss in real-time and apply the ANN prediction model to a 150 kJ self-made HTS SMES. The ANN prediction model is composed of a multi-scale model and an ANN. An ac loss data set calculated with the multi-scale model is used to train the ANN, and a validation data set is used to test the accuracy of the ANN after training. By comparing with the homogenization model, we find that the multi-scale model is reliable when used for ac loss calculation and suitable for building ac loss data set due to its high calculation speed. The ANN prediction model is verified to be reliable for fast estimation of the ac loss, and its accuracy improves as the scale of the training data set increases. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for artificial neural network (ANN) inverse modeling and applications to microwave filters. In inverse modeling of a microwave component, the inputs to the model are electrical parameters such as $S$ -parameters, and the outputs of the model are geometrical or physical parameters. Since the analytical formula of the inverse input–output relationship does not exist, the ANN becomes a logical choice, because it can be trained to learn from the data in inverse modeling. The main challenge of inverse modeling is the nonuniqueness problem. This problem in the ANN inverse modeling is that different training samples with the same or very similar input values have quite different (contradictory) output values (multivalued solutions). In this paper, we propose a multivalued neural network inverse modeling technique to associate a single set of electrical parameters with multiple sets of geometrical or physical parameters. One set of geometrical or physical parameters is called one value of our proposed inverse model. Our proposed multivalued neural network is structured to accommodate multiple values for the model output. We also propose a new training error function to focus on matching each training sample using only one value of our proposed inverse model, while other values are free and can be trained to match other contradictory samples. In this way, our proposed multivalued neural network can learn all the training data by automatically redirecting contradictory information into different values of the proposed inverse model. Therefore, our proposed technique can solve the nonuniqueness problem in a simpler and more automated way compared with the existing ANN inverse modeling techniques. This technique is illustrated by inverse modeling and parameter extraction of four microwave filter examples. [ABSTRACT FROM AUTHOR]

One of the recent frontiers of the multiple objectives optimization strategies is the introduction of an artificial neural network in place of a more time-consuming numerical tool or approach to compute the cost functions. This paper describes the development of a multiple objectives optimization strategy for an electromagnetic bandgap common mode filter by using an artificial neural network. The network training, that is to say the evaluation of the internal weights and biases, is optimized by a genetic algorithm. The results are discussed and also compared with those stemming from an already developed multiple objective sequential optimization strategy applied to the same common mode filter. In both strategies, the cost functions are the same and the optimization algorithm is a differential evolutionary one. [ABSTRACT FROM PUBLISHER]

The first and foremost process in tea manufacturing, withering, is the foundation for producing good quality. Moisture plays an important role in the manufacturing process of tea to get the desired quality. In this paper, a novel in situ instrumentation technique is proposed and validated experimentally for prediction of moisture loss (ML) in the withering process. In the proposed technique, ML is predicted based on the inlet and the outlet relative humidity (RH) and temperature during the process of withering. Network capable smart sensor nodes are developed for the measurement of RH and temperature at the inlet and outlet of the withering trough. Architecture of the nodes and network is described. A scaled-down prototype of an enclosed trough is developed to perform withering of tea leaves. Based on the data measured by the system, ML is predicted by using artificial neural network. Nonlinear autoregressive model with exogenous inputs is used for predicting the ML. The predicted ML is compared with the actual amount of ML measured by weight loss. A total of nine experiments are conducted for nine batches of tea leaves. The data collection, their analysis and results are reported in this paper. The observed result shows a good agreement between the predicted and actual ML. The maximum mean error in prediction is −3.6%. [ABSTRACT FROM PUBLISHER]

The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model. [ABSTRACT FROM AUTHOR]

A global and real-time control with embedded hardware artificial neural network (ANN) for imbalance current suppression of parallel connected insulated gate bipolar transistors (IGBTs) is first proposed in this paper. This method focuses on control strategy and control execution. The former one is realized by porting the ANN-based PID (ANN-PID) strategy in the control loop to yield the real-time and adaptive characteristics without IGBT quantity limitation. The latter one is realized by designing the IGBT gate quantity of charge regulator (GQR) to execute the command from ANN-PID controller. The evaluation of ANN-PID algorithm results 0.023% mean error in IGBT current control that reveals the feasibility of the proposed method. A full prototype with FPGA-based hardware accelerator for ANN-PID computing, including the designed GQR circuit, has been built for realization and qualification in a buck converter with parallel connected IGBTs. The experimental results show that the performance of the proposed method in imbalance current suppression is improved about 3.5–5.5 times as the load increase from low to high with the advantage of immunity to load change and the current imbalance can be suppressed within 4%. [ABSTRACT FROM AUTHOR]

This paper proposes the use of an artificial neural network (ANN) for solving one of the ongoing research challenges in finite set-model predictive control (FS-MPC) of power electronics converters, i.e., the automated selection of weighting factors in cost function. The first step in this approach is to simulate a detailed converter circuit model or run experiments numerous times using different weighting factor combinations. The key performance metrics [e.g., average switching frequency ($f_{{\rm sw}}$) of the converter, total harmonic distortion, etc.] are extracted from each simulation. This data is then used to train the ANN, which serves as a surrogate model of the converter that can provide fast and accurate estimates of the performance metrics for any weighting factor combination. Consequently, any arbitrary user-defined fitness function that combines the output metrics can be defined and the weighting factor combinations that optimize the given function can be explicitly found. The proposed methodology was verified on a practical weighting factor design problem in FS-MPC regulated voltage source converter for uninterruptible power supply system. Designed weighting factors for two exemplary fitness functions turned out to be robust to load variations and to yield close to expected performance when applied both to detailed simulation model (less than 3% error) and to experimental test bed (less than 10% error). [ABSTRACT FROM AUTHOR]

This paper describes the implementation and experimental verification of an instrument fault detection (IFD) scheme for stroke sensors. In the scheme, a nonlinear autoregressive with exogenous inputs (NARX) neural network works as a soft sensor for the generation of residuals, while a rule-based decision maker provides the fault detection and classification. The scheme was thought to be implemented in the firmware of central units for the control of semiactive suspension systems for motorbikes. Execution times compatible with the real-time application constraints are reached through straightforward programing rules and suitable code optimization. These times were obtained with machine parameter values (such as clock frequency and absorbed current) far below the upper limit of the range, thanks to the adoption of a programing methodology specially designed for the real-time implementation of diagnostic schemes on general-purpose microcontrollers (MCUs). The diagnostic performance of the scheme was verified through an experimental campaign, carried out on a motorcycle featured with electronic controlled semiactive suspensions. Promptness and reliability in detecting the most common faults of the rear stroke sensor resulted fully compatible with actual applications’ expected values. [ABSTRACT FROM AUTHOR]

This paper presents two algorithms, both based on the time of flight (ToF) of scattered waves, to locate defect in an anisotropic woven-fabric carbon fiber reinforced polymer (CFRP) plate. The first algorithm uses a probabilistic approach by constructing a probability matrix. Each element of this matrix is associated with a location and represents the probability of existing a defect at the corresponding spatial coordinates of the plate. For the probability matrix method, localization results are influenced by manually chosen parameters and by the anisotropy of the CFRP plate. The second algorithm, based on artificial neural networks (ANNs), enables us to improve the accuracy of locating defects. With this ANN method, the anisotropic feature can be “learned” by the neural network from training data. The ToF of scattered waves obtained from three sensor pairs were used directly as inputs of the neural network. The spatial coordinates of the defect are the ANN outputs. The difficulty of obtaining sufficient experimental data for ANN training was surpassed by using added blocks on the surface of the plate under test to simulate defects. This scheme was validated and proved to be effective by comparing the scattered waves from a delamination and from an added block. Conclusions have been drawn by comparing the two methods. The localization results obtained by the ANN algorithm are proved to be better. [ABSTRACT FROM AUTHOR]

This paper investigates the opportunity for energy saving in a three-phase induction motor (IM) driving pump load and proposes an improved loss model control (LMC). Compared with other power-loss reduction algorithms for IM, the presented one has the advantages of fast and smooth flux adaptation, high accuracy, and versatile implementation. The performance of LMC depends mainly on the accuracy of modeling the motor drive and losses. In this paper, the derived loss model considers the surplus power loss caused by inverter voltage harmonics using closed-form equations and also includes the magnetic saturation. An artificial neural network controller is synthesized and trained offline to determine the optimal flux level that achieves maximum drive efficiency. The drive's voltage and speed control loops are connected via the actual stator frequency, making the scheme comparatively reliable. Simulation and experimental studies are performed on 5.5-kW test motor using the proposed control method. The test results are provided and compared with the fixed flux operation to validate the effectiveness. [ABSTRACT FROM PUBLISHER]

In this paper, an artificial neural network (ANN) based method is developed for quickly estimating the long-term voltage stability margin. The investigation presented in the paper showed that node voltage magnitudes and the phase angles are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. If the voltage magnitudes and phase angles are obtained in real-time from phasor measurement units (PMUs) using the proposed method, the voltage stability margin can be estimated in real time and used for initiating stability control actions. Finally, a suboptimal approach to determine the best locations for PMUs is presented. Numerical examples of the proposed techniques are presented using the New England 39-bus test system and a practical power system which consists of 1844 buses, 746 load buses, and 302 generator buses. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and novel method of designing and developing high-linearity linear variable differential transformer (LVDT)-based displacement sensing systems. Conventionally, precise adjustment of windings is made to enhance the linearity range of LVDTs. The tedious job of pitch adjustment of windings of LVDTs can be overcome by using the proposed method. A functional link artificial neural network has been successfully used in this paper for nonlinear compensation of the LVDT. The effectiveness of the proposed method is demonstrated through computer simulation with the experimental data of two different LVDT. The complete algorithm with the practical setup for the development of a linear LVDT is presented in this paper. [ABSTRACT FROM AUTHOR]

The human arm exhibits outstanding manipulability in executing various tasks by taking advantage of its intrinsic compliance, force sensation, and tactile contact clues. By examining human strategy in controlling arm impedance, we may be able to understand underlying human motor control and develop control methods for dexterous robotic manipulation. This paper presents a novel method for estimating multijoint stiffness by using electromyogram (EMG) and an artificial neural network model. The artificial network model developed in this paper relates EMG data and joint motion data to joint stiffness. With the proposed method, the multijoint stiffness of the arm was estimated without complex calculation or specialized apparatus. The feasibility of the proposed method was confirmed through experimental and simulation results. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is always a popular topic in the electric power industry because of its essentiality in energy system planning and operation. In the deregulated power system, an improvement of a few percentages in the prediction accuracy would bring benefits worth of millions of dollars, which makes load forecasting become more important than ever before. This paper focuses on the short-term load forecasting for a power system in the U.S., where several alternative meteorological forecasts are available from different commercial weather services. To effectively take advantage of the alternative meteorological predictions in the load forecasting system, a new comprehensive forecasting methodology has been proposed in this paper. Specifically, combining forecasting using adaptive coefficients is applied to share the strength of the different temperature forecasts in the first stage, and then, ensemble neural networks have been used to improve the model's generalization performance based on bagging. The proposed load forecasting system has been verified by using the real data from the utility. A range of comparisons with different forecasting models have been conducted. The forecasting results demonstrate the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

The development of wind power generation has rapidly progressed over the last decade. With the advancement in wind turbine technology, wind energy has become competitive with other fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of wind power. The current practice ignores wind generation capacity in the unit commitment (UC), which discounts its usable capacity and may cause operational issues when the installation of wind generation equipment increases. To ensure system reliability, the forecasting uncertainty must be considered in the incorporation of wind power capacity into generation planning. This paper discusses the development of an artificial-neural-network-based wind power forecaster and the integration of wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this paper. [ABSTRACT FROM AUTHOR]

Power distribution systems play an important role in modern society. When distribution system outages occur, fast and proper restorations are crucial to improve the quality of services and customer satisfaction. Proper usages of outage root cause identification tools are often essential for effective outage restorations. This paper reports on the investigation and results of two popular classification methods: logistic regression (LR) and artificial neural network (ANN) applied on power distribution fault cause identification. LR is seldom used in power distribution fault diagnosis, while ANN has been extensively used in power system reliability researches. This paper discusses the practical application problems, including data insufficiency, imbalanced data constitution, and threshold setting that are often faced in power distribution fault cause identification problems. Two major distribution fault types, tree and animal contact, are used to illustrate the characteristics and effectiveness of the investigated techniques. [ABSTRACT FROM AUTHOR]

Conventional PI controllers are vulnerable to changes in parameters and are difficult to tune. In this work, an artificial neural network (ANN) based controller is developed for the robust operation of a single-phase modified packed U-cell five-level inverter (MPUC-5) for solar PV application under variable insolation conditions. An MPUC-5 is a converter with a main and an auxiliary dc link of equal magnitude; although five-level operation is also still feasible with different voltages also. The maximum power point (MPP) of a PV array changes with the variation in the solar insolation. This results in a variable voltage at the output of the boost converter while maintaining the load line at the MPP. Consequently, the fundamental value of the output of the MPUC-5 also tends to change. Thus, it is required to produce angles that commit to an ac output voltage with a constant fundamental value and constrained to a minimum total harmonic distortion along with a third-order harmonic mitigation as per the grid codes, irrespective of the change in the dc-link voltages. A genetic algorithm is employed for this purpose. A large dataset is prepared for two-angle and four-angle operation of MPUC-5 under various dc-link voltages and constraints with which an ANN-based controller is trained. A neural network with a hidden layer is trained with the backpropagation technique; and once a correlation is developed, the network can be operated for a wide range of operating conditions. The robustness of the controller is verified through simulation in MATLAB/Simulink environment and validated by experimental emulation in an hardware in loop environment. [ABSTRACT FROM AUTHOR]

This paper investigates the use of non-linear autoregressive exogenous (NARX) artificial neural networks (ANNs) to achieve black-box average dynamic models of dc-dc converters capable of capturing the main converter non-linearities. Non-linearities may include, for example, dynamic behavior variations due to changes of operating point or operating mode (e.g., discontinuous conduction mode, continuous conduction mode). This paper presents design guidelines for determining the NARX-ANN architecture and the dataset to be used in the training process. Dataset definition includes the choice of the perturbations for stimulating the aimed system behaviors and optimizations for dataset size reduction. The proposed approach is first derived for a dc-dc boost converter. To verify the generality of the proposed method, the same methodology is also applied to a Ćuk converter. In both cases, the proposed NARX-ANN modeling provided accurate results, with only limited deviations observed in the time-domain responses to step variations of duty-cycle and output current. The proposed model provided accurate small-signal behavior under different operating conditions. The validity of the approach is evaluated experimentally by considering a boost converter prototype.

Nonlinear hyperspectral unmixing (HSU) plays a key-role in understanding and quantifying the physical-chemical phenomena occurring over geometrically complex fields of view. Nonlinear HSU methods that do not rely on prior knowledge of the ground truth to analyze the scene are especially interesting. However, they can be affected either by overfitting or performance degradation provided by inaccurate setting of unmixing parameters. In this paper, we introduce a new nonlinear HSU architecture which aims at taking advantage of the benefit provided by the combination of polytope decomposition (POD) method together with artificial neural network (ANN)-based learning. Specifically, ANN is able to efficiently estimate the order $p$ of the nonlinearity provided by the given scene even without the thorough knowledge of the ground truth. The ANN-based learning is used to feed the POD in order to deliver accurate unmixing based on a $p$-linear polynomial model. Experimental results over simulated and real scenes show promising performance of the proposed framework. [ABSTRACT FROM PUBLISHER]

In this paper, a new method to discriminate between mechanical defects and electrical faults, as two major faults in power transformer windings, is proposed. In the first step, the detailed model of a real 1.2 MVA transformer winding is developed using geometrical dimensions and specifications. Thereafter, the frequency response characteristics are obtained for intact and defected cases using EMTP/ATP. In the next step, some features based on cross-correlation and other mathematical patterns are selected from the obtained signals. These features are then used to train an ANN classifier. The proposed method is able to precisely discriminate among disc-to-disc short circuit faults, radial deformation and axial displacement defects and determine their location or extent with a good accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a practical fault diagnosis algorithm is presented to realize multiple open-circuit fault diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault diagnosis algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification algorithms, the proposed algorithm is simple and stable to realize the multiple switches fault diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault diagnosis algorithm is verified by the experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber glass core, cracks in the fiberglass core, and small metallic inclusion between the fiber core and shank were inspected. The microwave inspection system uses an open-ended rectangular waveguide sensor operating in the near-field at a frequency of 24 GHz. A data acquisition system was used to record the measured data. ANN was trained to classify the different types of defects. The results showed that all defects were detected and classified correctly with high recognition rates. [ABSTRACT FROM AUTHOR]

Coriolis flowmeters are well established for the mass flow measurement of single-phase flow with high accuracy. In recent years, attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated into Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 to 14500 kg/h and a gas volume fraction between 0% and 30%. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-ANN), SVM, and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN, and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline, while 96.17% of the results are within ±1% on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on the horizontal and vertical installations, respectively. [ABSTRACT FROM PUBLISHER]

In the current scenario, the foremost use of microwave absorbers, such as stealth applications and electromagnetic (EM) wave shielding, demands thin and broad bandwidth microwave absorber. In this paper, an attempt is made to fabricate a “cost-effective” composite material, which has good absorption properties, and furthermore, to reduce the coating thickness, the multi-layering approach has been adopted. Because of the excellent magneto-dielectric properties, Fe and Al metal powders were chosen. To see the effect of particle size over microwave absorption property of Fe–Al hybrid nano-composites, the composite was milled for 40 h with an interval of 5 h using the mechano-chemical route. The phase analysis, morphology, and magnetic studies have been carried out for prepared samples and correlated with EM wave properties. The permittivity and permeability values in the frequency range of 4–14 GHz were determined using coaxial reflection/transmission method, and reflection loss (RL) has also been computed. The broad bandwidth of 9.35 GHz (4.65–14 GHz) has been achieved for 40 h milled nano-composite (size ~40 nm) with a coating thickness of 3 mm. For the reduction in the coating thickness, the multi-layering approach has been applied, and an artificial neural network-based method has been proposed to predict each layer thickness and material sequence. The computed results of RL for two-layer absorber exhibit a reduction in the thickness from 3 to 1.96 mm. [ABSTRACT FROM PUBLISHER]

This paper proposes an artificial neural network (ANN)-based reinforcement learning (RL) maximum power point tracking (MPPT) algorithm for permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs). The proposed MPPT algorithm first learns the optimal relationship between the rotor speed and electrical power of the PMSG through a combination of the ANNs and the Q-learning method. The MPPT algorithm is switched from the online RL to the optimal relation-based online MPPT when the maximum power point is learned. The proposed online learning algorithm enables the WECS to behave like an intelligent agent with memory to learn from its own experience, thus improving the learning efficiency. The online RL process can be reactivated any time when the actual optimal relationship deviates from the learned one due to the aging of the system or a change in the environment. Simulation and experimental results are provided to validate the proposed ANN-based RL MPPT control algorithm for a 5-MW PMSG-based WECS and a small emulated PMSG-based WECS, respectively. [ABSTRACT FROM PUBLISHER]

This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg--Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura--Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feedforward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg--Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained. [ABSTRACT FROM AUTHOR]

In this paper, a novel extended Hammerstein model is presented to accurately mimic the dynamic nonlinearity of wideband RF power amplifiers (RFPAs). Starting with a conventional Hammerstein model scheme, which fails to predict the behavior of the RFPA with short-term memory effects, two areas of improvements were sought and found to allow for substantial improvement. First, a polar feed-forward neural network (FFNN) was carefully chosen to construct the memoryless part of the model. The error signal between the output and the input signal of the memoryless sub-model was then filtered and then post-injected at the model output. This extra branch, when compared to the conventional Hammerstein scheme, allowed for an extra mechanism to account for the memory effects due to dispersive biasing network that was present otherwise. The excellent estimation capability of the polar FFNN together with the additional filtered error signal post-injection led to remarkable accuracy when modeling two different RFPAs both driven with four-carrier wideband code division multiple access signals. Despite its simple topology and identification procedure, the extended Hammerstein model demonstrated is capable in accurately predicting the dynamic AM/AM and AM/PM characteristics and the output signal spectrum of the RFPA under test. [ABSTRACT FROM AUTHOR]

The detection and characterization of macroscopic cracks inside dielectric materials is an important practical issue. Thus, there is a need to establish evaluation techniques; which can be used to characterize buried cracks; indeed, the knowledge of the geometrical configuration of a hidden crack is a key factor for fatigue crack engineering. Therefore, a microwave method for nondestructive characterization of macroscopic cracks inside dielectric materials is presented in this paper. This nondestructive and noncontact technique is based on the determination of the near-field reflection coefficient of an open-ended rectangular waveguide. The measurements are achieved by means of a microwave six-port-based system that operates at 35 GHz. We show that relatively small defects are detectable and demonstrate that the association of signal processing tools to this characterization method enables the retrieval of the crack profile in an acceptable manner. The reconstruction of a 1-D buried crack profile is performed by means of a multiple-multilayer-perceptron (MLP) approach. Several cases are investigated to demonstrate the capabilities of the method. [ABSTRACT FROM AUTHOR]

This paper aims at detecting and characterizing inclusions in concrete structures by inverting ground-penetrating radar (GPR) data. First, the signal is preprocessed using the principal component analysis (PCA) and then used to train an artificial neural network (ANN). The GPR data consists of 1200 time steps. Using PCA, the data can be compressed to 286 dimensions without losing any information. Moreover, with 99.99% of the original variance the data needs only 139 dimensions. This dimensional reduction makes the ANN training easier and faster. The ANN were trained to find the buried inclusions characteristics-depth and radii-considering a nonhomogenous host medium by inverting the preprocessed data. The results show that the expected maximum error was kept under 1%, which is a remarkable result, since the host medium is nonhomogenous. [ABSTRACT FROM AUTHOR]

As is well known ill statistics, the resulting linear regressors by using the rank-based Wilcoxon approach to linear regression problems are usually robust against (or insensitive to) outliers. This motivates us to introduce in this paper the Wilcoxon approach to the area of machine learning. Specifically, we investigate four new learning machines, namely Wilcoxon neural network (WNN), Wilcoxon generalized radial basis function network (WGRBFN), Wilcoxon fuzzy neural network (WFNN), and kernel-based Wilcoxon regressor (KWR). These provide alternative learning machines when faced with general nonlinear learning problems. Simple weights updating rules based on gradient descent will be derived. Some numerical examples will be provided to compare the robustness against outliers for various learning machines. Simulation results show that the Wilcoxon learning machines proposed in this paper have good robustness against outliers. We firmly believe that the Wilcoxon approach will provide a promising methodology for many machine learning problems. [ABSTRACT FROM AUTHOR]

In recent years, using distributed fiber-optic sensors based on Brillouin scattering, for monitoring pipelines, tunnels, and other constructional structures have gained huge popularity. However, these sensors have a low signal-to-noise ratio (SNR), which usually increases their measurement error. To alleviate this issue, ensemble averaging is used which improves the SNR but in return increases the measurement time. Reducing the noise by averaging requires hundreds or thousands of scans of the optical fiber; hence averaging is usually responsible for a large percent of the entire system latency. In this paper, we propose a novel method based on artificial neural network for SNR enhancement and measurement acceleration in distributed fiber-optic sensors based on the Brillouin scattering. Our method takes the noisy Brillouin spectrums and improves their SNR by 20 dB, which reduces the measurement time significantly. It also improves the accuracy of the Brillouin frequency shift estimation process and its latency by more than 50% in comparison with the state-of-the-art software and hardware solutions. [ABSTRACT FROM AUTHOR]

This paper deals with the optimization of stress distribution on and around electrode-spacer arrangements for ensuring economical and higher reliability of gas-insulated systems. The optimization technique adopted in this paper is the artificial neural network-aided simulated-annealing (SA) algorithm. By coupling a trained neural net with the annealing algorithm, the execution speed of the latter is greatly enhanced to evaluate the optimum values for the design parameters of the electrode-spacer arrangements compared to calculating the cost function via the entire process for field calculation at every move of the optimization algorithm. The convergence of the optimization algorithm has also been compared statistically with the genetic algorithm. The results presented in this paper show that optimized electrode-spacer contours have been obtained with an acceptable degree of accuracy using the neural-network-aided SA algorithm. The statistical analysis shows a promising result for the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an artificial neural network (ANN)-based coordination control scheme for under load tap changing (ULTC) transformer and STATCOM. The objective of the coordination controller is to minimize both the amount of tap changes of the transformer and STATCOM output while maintaining an acceptable voltage magnitude at the substation bus. The coordination controller is designed to substitute for a classical ULTC mechanism by utilizing active and reactive powers, tap position, and STATCOM output. A competitive ANN is used as a classifier for tap positions and trained by a proposed iterative condensed nearest neighbor (ICNN) rule. [ABSTRACT FROM AUTHOR]

A complete solution from parameter extraction to large-signal electrothermal model generation for gallium nitride (GaN) HEMTs is presented in this paper with the consideration of trapping deduced gate and drain lag effects. The extrinsic parasitic parameters are extracted by multibias hot-FET optimization using artificial bee colony algorithm. New terminal charge ( Q\mathrm {gs} , Q\mathrm {gd} , and Q\mathrm {ds}) models with temperature dependence are proposed to better characterize the GaN devices. Physical mechanisms of the electrothermal and trapping effects have been investigated, and the artificial neural network (ANN) is exploited to construct the drain current based on pulsed $I$ – $V$ (PIV) measurements. Besides the instantaneous terminal voltages, additional three auxiliary variables are employed to describe the memory effects of GaN HEMT: channel temperature, gate trapping state, and drain trapping state. These variables are identified from PIVs to compose the input layer of the ANN, while in the simulator, they are captured by the thermal and two envelop tracking subcircuits. These physical auxiliary variables together with the ANN technology enable unlimited fitting sets of PIVs with satisfying accuracy. Single-tone and two-tone on-wafer measurements are conducted for the verification, and a good agreement has been achieved between the measurements and simulations. [ABSTRACT FROM AUTHOR]

Ransomware has been one of the biggest cyber threats against consumers in recent years. It can leverage various attack vectors while it also evolves in terms of finding more innovative ways to invade different cyber security systems. There have been many efforts to detect ransomware within the workforce and academia leveraging machine learning algorithms, which has shown promising results. Accordingly, there is a considerably large body of literature addressing various solutions on how ransomware threats can be detected and mitigated. Such large and rapidly growing scientific and technical materials start to make it difficult in knowing the actual ML algorithm(s) being used. Hence, the aim of this paper is to give insight about ransomware detection frameworks and those ML algorithms which are typically being used to extract ever-evolving characteristics of ransomware. In addition, this study will provide the cyber security community with a detailed analysis of those frameworks. This will be augmented with information such as datasets being used along with the challenges that each framework may be faced with in detecting a wide variety of ransomware accurately. To summarize, this paper delivers a comparative study which can be used by peers as a reference for future work in ransomware detection.

Fine particle matter (PM2.5) has been receiving increasing attention by the government due to its considerable adverse health effects, especially in the north part of China. Even though a number of techniques of estimating PM2.5 exposure have been developed, what is still lacking is a systematic comparison of commonly used techniques based on classical statistics, artificial intelligence, and geostatistics. To address this need, the land use regression (LUR), the artificial neural networks (ANN), and the Bayesian maximum entropy (BME) techniques were all used to map the space–time PM2.5 concentration distribution in the highly polluted Jing-Jin-Ji region (Huabei plain of North China) during the period June 2015–May 2016. The tenfold cross-validation analysis and the entropic information theory were used to evaluate numerically the performance of the three techniques at monthly, seasonal, and annual time scales. Our results showed that the performance of each mapping technique was affected by the temporal scale and the degree of spatial heterogeneity. All three techniques were suitable for low temporal resolution (annual) datasets with low spatial variability. BME also showed a noticeable ability to analyze higher temporal resolution (monthly) datasets exhibiting high spatial heterogeneity. BME involved a single dependent variable (PM2.5) and generated complete (full-coverage) space–time PM2.5 maps, whereas LUR and ANN produced incomplete maps because of lacking independent variables (such as satellite data). Due to its self-learning feature, ANN showed better modeling performance than LUR and produced more informative maps. Overall, the ANN and BME techniques perform better than the LUR technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel preprocessing scheme to improve the prediction of sand fraction from multiple seismic attributes such as seismic impedance, amplitude, and frequency using machine learning and information filtering. The available well logs along with the three-dimensional (3-D) seismic data have been used to benchmark the proposed preprocessing stage using a methodology that primarily consists of three steps: 1) preprocessing; 2) training; and 3) postprocessing. An artificial neural network (ANN) with conjugate-gradient learning algorithm has been used to model the sand fraction. The available sand fraction data from the high-resolution well logs have far more information content than the low-resolution seismic attributes. Therefore, regularization schemes based on Fourier transform (FT), wavelet decomposition (WD), and empirical mode decomposition (EMD) have been proposed to shape the high-resolution sand fraction data for effective machine learning. The input data sets have been segregated into training, testing, and validation sets. The test results are primarily used to check different network structures and activation function performances. Once the network passes the testing phase with an acceptable performance in terms of the selected evaluators, the validation phase follows. In the validation stage, the prediction model is tested against unseen data. The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume. Finally, a postprocessing scheme using 3-D spatial filtering is implemented for smoothing the sand fraction in the volume. Prediction of lithological properties using this framework is helpful for reservoir characterization (RC). [ABSTRACT FROM PUBLISHER]