Nominal Parameter Values Research Articles

Sensitivity of mechanical properties to processing defects: Is tensile testing an appropriate metric for laser beam metal powder bed fusion machine qualification?

The Laser-based Powder Bed Fusion of Metals (PBF-LB/M) process has shown potential in the manufacturing of critical flight components, yet also has faced difficulties in achieving reproducibility. One potential reason for this is that tensile testing, which is one of the methods traditionally used to ascertain quality in the additive manufacturing (AM) industry, is insufficient for capturing underlying flaws in an additively manufactured component. To quantify the ability of tensile testing to detect underlying flaws in a component, a generalized full factorial experiment was performed where the tensile properties of UNS N07718 were measured across several types of build conditions. Combinations of differently sized bars and surface conditions were investigated. Defects were induced by amplifying the polygon delay parameter, hence prolonging the dwell time of the laser in the contours. The authors found that those tensile bars that experienced increased polygon delay had ∼10x more pores than those built with nominal parameter values while still maintaining an average porosity of <0.25 % (i.e., a total part porosity that is acceptable by NASA-STD-6030). Regardless of the specimen geometry and surface condition, differences between parts which had undergone polygon delay and those that had not were either statistically insignificant or were too small to be relevant, indicating that tensile testing may not be an effective metric for identifying the quality of a PBF-LB/M process.

Noise and Parameter Mismatch in a Ring Network for Sensing Extremely Low Electric Fields

Over the past years, we have exploited the bistability features that are commonly found in many individual sensors to develop a network-based [ Acebrón et al. , 2003 ; Bulsara et al. , 2004 ; In et al. , 2003a ; In et al. , 2003b ; In et al. , 2005 ; In et al. , 2006 ; In et al. , 2012 ; Palacios et al. , 2005 ] approach to modeling, designing, and fabricating extremely sensitive magnetic- and electric-field sensors capable of resolving field changes as low as 150[Formula: see text]pT and 100[Formula: see text]fAmps, respectively. Higher sensitivity is achieved by exploiting the symmetry of the network to create infinite-period bifurcations that render the ensuing oscillations highly sensitive to symmetry-breaking effects from external signals. In this paper, we study the effects of noise on the response of a network-based electric-field sensor as well as the effects of parameter mismatch, which appear naturally due to material imperfections and noise. The results show that Signal-to-Noise Ratio (SNR) increases sharply near the onset of the infinite-period bifurcation, and they increase further as the coupling strength in the network increases while passing the threshold that leads to oscillatory behavior. Overall, the SNR indicates that the negative effects of highly contaminated signals are well-mitigated by the sensitivity response of the system. In addition, computer simulations show the network-based system to be robust enough to mismatches in system parameters, while the deviations from the nominal parameter values form regions where the oscillations persist. Noise has a smoothing effect over the boundaries of these regions.

Neural Robust Control for a Mobile Agent Leader–Follower System

A controller employing a combined new strategy of output feedback linearization and a recurrent high-order neural network (RHONN) adaptive approach for a mobile agent leader–follower system is presented. The controller structure is based on feedback linearization; then, a scheme of lumping uncertainties which are estimated via the RHONN is incorporated; with this estimate, the controller is able to produce a robust control action for mobile agents so they track a prescribed reference trajectory. Moreover, the nonlinear system part is transformed into a linearizable one; then, a specific function lumps all the nonlinearities, uncertain parameters, and unmodeled dynamics of the system; this overall function is estimated via the RHONN. Thus, both parametric uncertainties and unmodeled dynamics between agents can be compensated via the controller, and, subsequently, follower agents track the reference provided by the leader. The obtained controller is such that the estimation scheme is not based on high-gain controllers. Here, it is underlined that the main contribution consists of designing a nonlinear controller and combining it with an RHONN to estimate the nonlinear uncertainties in the leader–follower system. This control action includes robust features provided by the online recurrence and the nonlinear base of the neural network in which not general but specific parametric disturbances and unmodeled discrepancies are identified or compensated. For this control scheme, only nominal values of the system parameters are required, as well as the velocities of the agents. Numeric simulation of the model and designed tracking control are carried out in which the control law is applied to a two-wheeled differential mobile robot model, obtaining satisfactory results for tracking angular velocities of the wheels.

Electrical Power Systems Reinforcement through Overall Contingency Index Analysis and Improvement

This paper analyzes the behavior of an electrical power system when N-1 contingencies occur in the transmission stage, which can be produced by incorrect operation of the protection relays, phenomena of natural origin, or increased loadability, which affect the operation and reliability of the electrical system. The operation output of a transmission line results in the variation of the nominal values of the electrical parameters involved because they disturb the stability of the generation, transmission systems, and the supply of electrical energy to the loads, such as voltages and angles of the nodes and the active and reactive power of the system. The proposed methodology was based on analyzing the different electrical parameters of the power system, quantifying the contingency index in a state of regular operation, and comparing it to operation in contingency N-1, with which the most severe contingency was determined and, therefore, achieved; identifying contingencies that can cause system collapses; improving the contingency index from 23.08555 to 22.9276624 for the L16–19 contingency and to 22.9795235 for the L21–22 contingency, which are the most severe contingencies determined with the proposed methodology. To test the proposed methodology, the IEEE 39 bus-bar test system was considered, and the elements that should be implemented to avoid the vulnerability of the power system to N-1 contingencies were determined.

Design and analysis of optimal AGC regulator for multi‐area power systems with TCPS and energy storage unit in deregulated environment

AbstractIn today's electric power network, fast changes are the norm, and auxiliary services such as automatic generation control (AGC) play a crucial role in maintaining the quality of the power supply. AGC ensures a balance between power generation, demand, and losses to sustain frequency stability and variation in tie‐line power within a set limit, even when the load changes. To accomplish this, it is always beneficial to consider new approaches to controlling the situation. This paper introduces the design of new AGC regulators that consider deviation in DC tie‐line power as an extra control variable for the turbine controller. In this study, three different deregulated power systems (containing two control areas), namely three thermal generations, the combination of one thermal and two hydro generations, and three hydro generations, including two power distribution companies (DISCOs) in each control area. These optimal AGC regulators are implemented in the proposed thermal–thermal–thermal system to carry out the various power contracts. The results have shown that the dynamic outcomes meet the AGC standards. To improve further dynamic results and the proposed systems' stability margins by incorporating redox flow batteries (RFB). In actual operating conditions, the system parameters do not remain constant due to the aging effect, assumptions made in simplifying the mathematical model, etc. Thus, ±50% deviations in the nominal value of system parameters to assess how well the optimal AGC regulators perform in the system under investigation. The suggested realistic AGC system incorporates many parameter fluctuations and works well with the optimal AGC regulators developed for the proposed plans. This study expanded to include a two‐area, thermal‐hydro‐hydro (THH) system under a deregulated framework connected via an asynchronous transmission link with or without a thyristor‐controlled phase shifter (TCPS) and RFB. The genetic algorithm (GA) can solve many issues and has global search, flexibility to varied problem types, intrinsic parallelism, and the ability to handle massive, complex search spaces. Thus, a two‐area deregulated hydro‐hydro‐hydro system with parallel tie‐lines utilizes GA‐PID, GA‐FOPID, and GA‐(1 + PI)‐FOPID controllers. Moreover, a random load disturbance (RLD) is employed in each section of the offered approach to show the resilience and elite performance of the proposed control strategy. To produce various dynamic reactions in the plans, MATLAB software version R2013a is employed.

Shortest path network interdiction with asymmetric uncertainty

AbstractThis paper considers an extension of the shortest path network interdiction problem that incorporates robustness to account for parameter uncertainty. The shortest path interdiction problem is a game of two players with conflicting agendas and capabilities: an evader, who traverses the arcs of a network from a source node to a sink node using a path of shortest length, and an interdictor, who maximizes the length of the evader's shortest path by interdicting arcs on the network. It is usually assumed that the parameters defining the network are known exactly by both players. We consider the situation where the evader assumes the nominal parameter values while the interdictor uses robust optimization techniques to account for parameter uncertainty or sensor degradation. We formulate this problem as a nonlinear mixed‐integer semi‐infinite bilevel program and show that it can be converted into a mixed‐integer linear program with a second order cone constraint. We use random geometric networks and transportation networks to perform computational studies and demonstrate the unique decision strategies that our variant produces. Solving the shortest path interdiction problem with asymmetric uncertainty protects the interdictor from investing in a strategy that hinges on key interdictions performing as promised. It also provides an alternate strategy that mitigates the risk of these worst‐case possibilities.

Adaptive Control Method for Conically Shaped Dielectric Elastomer Actuator With Different Loads

In this paper, we present an adaptive control method for a conically shaped dielectric elastomer actuator (CSDEA) with different loads to achieve its tracking control objective. Firstly, a dynamic model of the CSDEA is constructed to describe its asymmetrical, rate-dependent hysteresis behavior and creep behavior simultaneously. Then, an offline parameter identification method based on the nonlinear-least-squares algorithm is presented to obtain the nominal values of the model parameters of the CSDEA corresponding to the load of 200 (g). The obtained values are regarded as the initial values of the adaptive online parameter identification. Next, an adaptive online parameter identification method (AOPIM) based on the least-mean-square algorithm is proposed, which can dynamically calculate and update the model parameter values to cope with the load changing of the CSDEA. Lastly, based on two kinds of analytical inverses of the dynamic model and the proposed AOPIM, two adaptive inverse compensators are respectively designed to realize the tracking control of the CSDEA. The control experiments with different desired trajectories and different loads are implemented to demonstrate the effectiveness of the presented adaptive control method. Since the root-mean-square errors of the results of all control experiments are lower than 2.7%, the presented method is remarkable from the perspective of the practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The dielectric elastomer material has beneficial properties of high energy density, low mass density, large deformation, fast response and good biological compatibility. Thus, the soft actuator based on the dielectric elastomer material (SADE) has shown great potentials for being used in soft robots. Nevertheless, the SADE has the complex nonlinear behaviors, which brings a big challenge for its precision control. To deal with this issue, some approaches have been presented in previous literatures. However, the load of the SADE is usually fixed in these studies. This paper focuses on the tracking control of the SADE with different loads. To this end, a dynamic model of the CSDEA is constructed, whose parameter values are dynamically calculated and updated to cope with the load changing of the CSDEA. Through calculating the analytical inverse of the dynamic model, two adaptive inverse compensators are developed. The control experimental results demonstrate that the presented adaptive control method is effective. Considering that the load of the SADE is usually diverse in practical applications, this study pays the way for the application of the SADE.

Identification of switched dynamic system for electric multiple unit train modeling

A new iterative algorithm is proposed to identify the high-speed train model which is described as a switched dynamic system including a fast switched nonlinear static subsystem followed by a non-switched linear dynamic subsystem. The non-switched property of the linear part leads to a more complex dynamics of the whole system, compared to the switched Hammerstein system which linear part switches synchronously with its nonlinear part. The proposed iterative algorithm alternates between identifying the switched nonlinear static subsystem and the linear dynamic subsystem. The switched nonlinear subsystem identification incorporates a quadratic error bound constraint with respect to nominal values of model parameters, and is formulated as a second-order cone program. Properties of convergence and asymptotic unbiasedness of the obtained estimates are proved under some necessary assumptions. With real data and simulation examples, the proposed iterative algorithm produces convergent and asymptotically unbiased estimates. In the simulation results, incorporating the quadratic error bounds results in a fast convergence rate in the presence of a small noise variance, and also produces a smaller identification error in the presence of colored noises with a non-zero mean and a large variance.

Влияние режимов дуговой автоматической сварки на геометрические параметры шва стыковых соединений из низкоуглеродистой стали, выполненных с применением экспериментального флюса

Introduction. The metallurgical industry in the territory of the Russian Federation has accumulated a significant amount of slags obtained during the smelting of steels and cast iron. The presence of slag dumps adversely affects the ecology of regions with metallurgical enterprises. When reducing iron from slags, the by-product becomes an oxide agglomerate, which can be considered as a flux composition for arc welding/surfacing under a layer of flux, fillers of powder wires, coatings of welding stick electrodes. The purpose of the work is to establish the possibility of arc welding using the flux obtained by the authors and to determine the optimal welding modes with the condition of achieving the geometric parameters of the seam according to GOST 8713-79 and the quality of the welded joint (absence of internal defects). In this paper, butt welded joints of sheet steel VSt3sp with a thickness of 5 mm obtained by automatic welding under a layer of flux at direct current with forced formation of a root roller on ceramic linings using flux from recycled metallurgical slag of an electric steelmaking enterprise are investigated. Automatic welding of flat specimens was carried out on a tractor-type ADF-1250 machine with a wire with a diameter of 3 mm, at a constant welding speed of 54 cm/min with varying current and arc voltage within 400–600 A and 27–37 V. The methods of investigation: Visual measuring and radiographic control, determination of deformation of specimens by laser scanning and computer processing of 3D models were used to evaluate the quality of welded joints. Statistical modeling in the form of a two-factor experiment was also used in the work, with obtaining adequate regression equations of the influence of welding modes on the geometric parameters of the seam: the height of reinforcement and the width of the seam on the front and back of the joint. Results and discussion. The possibility of obtaining welding fluxes from metallurgical slags of an electric steelmaking enterprise and its use for creating welded joints is shown. Optimal modes of arc welding of thin-walled sheet parts made of low-carbon steel with forced formation of a root roller on ceramic linings is established, ensuring the absence of internal defects in the form of pores, cracks and lacks of penetration, a minimum of residual deformations and compliance of the weld size with the requirements of the existing standard. The nominal values of the geometric parameters of the seam according to GOST 8713-79-C4 correspond to welding mode: welding speed 54 cm/min, welding current 550 A, arc voltage 30 V. The results of the work can be applied in metallurgical electric steelmaking enterprises producing low-carbon steel in the development of technologies for the use of welding materials from slag.

Analytical Solution of Time-Optimal Trajectory for Heaving Dynamics of Hybrid Underwater Gliders

Underwater vehicles have capacity limits for control inputs, within which their time-optimal trajectories (TOTs) can be formulated. In this study, the fastest trajectory for the depth control of a hybrid underwater glider (HUG) was found using buoyancy engines and propellers individually, and the decoupled heave dynamics of the HUG were defined using quadratic hydrodynamic damping. Because buoyancy engines always run at slow speeds, the buoyancy force was formulated based on the constant force rate of the engine. It was assumed that the nominal value of the heave dynamics parameters could be estimated; therefore, the analytical solution of heave dynamics could be formulated using the thrusting saturation and constant buoyancy force rate. Then, the shortest trajectory for depth control of the HUG could be established while considering the actuator saturation. To verify the effectiveness of the TOT in HUG heave dynamics, extensive tracking control simulations following the TOT were conducted. It was found that the proposed TOT helps the HUG reach the desired depth in the shortest arrival time, and its robust depth control showed good tracking performance in the presence of external bounded disturbances.

Robust Nonlinear Control of a Wind Turbine with a Permanent Magnet Synchronous Generator

This paper addresses the design of a robust nonlinear dynamic controller for a wind turbine. The turbine is equipped with a permanent magnet synchronous generator. The control problem involves tracking a suitable reference value for the turbine’s angular velocity, which corresponds to the wind speed. This issue is tackled by compensating for variations in the electrical and mechanical parameters present in the mathematical model. Additionally, the problem is approached under the assumption that wind speed cannot be directly measured, a fact verified in practical scenarios. This situation is particularly relevant for real-world applications, where only nominal parameter values are accessible and accurate wind speed measurement is challenging due to disturbances caused by the turbine or other factors, despite the use of appropriate sensors. To achieve precise tracking of the angular velocity reference, effective compensation of perturbation terms arising from parameter uncertainties and errors in wind estimation becomes crucial. To address this problem, a wind velocity estimator is employed in conjunction with high-order sliding mode parameter estimators, ensuring the turbine’s operation attains a high level of performance.

Forced convection heat transfer of concurrent Al2O3/PCM nanofluid flows in a concentric double tube

A trend toward high power and relatively small package sizes in electronic components has resulted in a dramatic increase in the corresponding heating flux; thus, the enhancement of internal convection has been a focus of attention. In this study, to improve the thermal efficiency of the internal flow, the forced-convection heat transfer of concurrent flows of functional working fluids through the outer annulus/inner tube of a concentric double tube was investigated experimentally and compared to that of an identical double tube filled with water, for which limited information is available. The fluids in the outer annulus/inner tube were 0.5% Al2O3-water nanofluid/pure water, 1.0% Al2O3-water nanofluid/pure water, and 1.0% Al2O3-water nanofluid/4.63% phase-change nanofluid. The nominal values of the main parameters were as follows: heating power = 120 W, 160 W, and 200 W; Reynolds number = 800, 1300, and 1700; and concurrent flow ratio = 0.1, 0.29, 0.45, 1.0, 1.6, and 2.38. The results indicated that when the outer annulus and the inner tube were filled with a 1.0% Al2O3-water nanofluid and a 4.63% PCM emulsion/or water, respectively, a high total flow rate and high flow ratio could suppress the rise in the wall temperature and increase the average heat transfer coefficients. Although using a functional working fluid may enhance heat transfer, this gain is considerably smaller than the increase in the pressure drop, resulting in the figures of merit for nearly all the considered cases being lower than 1.

Reinforcement Learning-Based Path Following Control with Dynamics Randomization for Parametric Uncertainties in Autonomous Driving

Reinforcement learning-based controllers for safety-critical applications, such as autonomous driving, are typically trained in simulation, where a vehicle model is provided during the learning process. However, an inaccurate parameterization of the vehicle model used for training heavily influences the performance of the reinforcement learning agent during execution. This inaccuracy is either caused by changes due to environmental influences or by falsely estimated vehicle parameters. In this work, we present our approach of combining dynamics randomization with reinforcement learning to overcome this issue for a path-following control task of an autonomous and over-actuated robotic vehicle. We train three independent agents, where each agent experiences randomization for a different vehicle dynamics parameter, i.e., the mass, the yaw inertia, and the road-tire friction. We randomize the parameters uniformly within predefined ranges to enable the agents to learn an equally robust control behavior for all possible parameter values. Finally, in a simulation study, we compare the performance of the agents trained with dynamics randomization to the performance of an agent trained with the nominal parameter values. Simulation results demonstrate that the former agents obtain a higher level of robustness against model uncertainties and varying environmental conditions than the latter agent trained with nominal vehicle parameter values.

Optimal control of a nonlinear state-dependent impulsive system in fed-batch process

Optimal control technique is crucial to improve the yield of microbial fermentation production. In this paper, we propose a nonlinear control system with state-dependent impulses, where the impulsive volume of feeding glycerol and the critical concentration of glycerol for occurring impulse are the control variables, to formulate 1,3-propanediol (1,3-PD) fed-batch production process. We also discuss a quantity of important properties for this control system. Then, we analyze the sensitivity of system state with respect to the kinetic parameters. We further propose a constrained optimal control model governed by the control system with state-dependent impulses. The existence of the optimal impulsive controls is established. For solving this problem, we utilize an exact penalty method to transform the problem into an optimization problem with only box constraints. Moreover, an improved differential evolution method is developed to seek the optimal impulsive strategy. Finally, numerical simulation results demonstrate that, by using the optimal impulsive strategies, final 1,3-PD concentration is considerably increased under the nominal parameter values and disturbances of kinetic parameters have significant effects on the optimal final 1,3-PD yield.

Adaptive Deadbeat Predictive Control for PMSM-based solar-powered electric vehicles with enhanced stator resistance compensation

This article introduces an innovative approach called Adaptive Deadbeat Predictive Control (ADPC), specifically designed to regulate the operation of a Permanent Magnet Synchronous Motor (PMSM) within a solar-powered electric vehicle (SEV). Considering the influence of factors such as magnetic saturation, material aging, and temperature fluctuations, discrepancies may arise between actual and nominal parameter values. Notably, the stator inductance, permanent magnet flux linkage, and stator resistance are crucial variables in this context. The article conducts an in-depth theoretical examination of the consequences of these parameter mismatches on the overall stability of the system. To mitigate the effects of stator resistance variability, an adaptive control methodology is proposed. This strategy hinges on real-time control adaptation, achieved by continuously estimating the resistance value through diligent consideration of the actual winding temperature. The proposed Adaptive Deadbeat Predictive Control framework is seamlessly implemented and simulated within the MATLAB Simulink environment. The simulation results eloquently validate the efficacy of the proposed control approach, showcasing its adeptness in mitigating the system’s sensitivity to stator resistance deviations, thereby fortifying the overall control resilience.

Frequency- and Time-Domain Yield Optimization of a Power Delivery Network Subject to Large Decoupling Capacitor Tolerances

Suboptimal design of power delivery networks (PDNs) may cause performance deterioration and severe functional failures on high-speed computer platforms. Voltage regulators (VRs) distribute controlled voltage in the PDN to the active devices, providing a steady power supply at a desired DC voltage level with an acceptable noise level or ripple. Unacceptable voltage drops can be caused by transient switching currents at the devices. Many decoupling capacitors are commonly used to lower the PDN impedance profile in order to reduce power supply noise and to supply fast transient current to switching devices. However, commercially available decoupling capacitors typically present large manufacturing variability. In this article, we first propose an optimization methodology that gradually finds the best compensation parameter values of a buck converter VR to meet suitable stability criteria. Simultaneously, the number of parallel decoupling capacitors in the PDN is minimized while meeting a frequency-domain impedance profile specification and a time-domain minimum voltage droop requirement under nominal parameter values. Finally, a statistical analysis, yield estimation, and yield optimization of the nominally optimized PDN subject to large decoupling capacitor tolerances is presented. We consider the impedance profile, transient voltage droop, and VR stability as the responses of interest for yield calculation.

Development of Verification Device for Multitarget Radar Velocimeter Based on Echo Signal Simulation Technology

Speeding is one of the leading causes of traffic crashes worldwide. Radar velocimeter is widely used in the capture monitoring of road overspeed violations, which can effectively reduce the probability of traffic accidents and protect people’s life and property safety to the greatest extent. As a new type of radar velocimeter, multitarget radar velocimeter (MTRV) can monitor the speed of more than two vehicles at the same time. However, the verification method and device of MTRV’s performance need to be studied. In order to solve the problem of performance verification for MTRV, a verification device based on echo signal simulation technology is developed in this paper. The measurement mechanism of MTRV with different performance including velocity, distance, and angle is first introduced. Then, a verification method based on the echo signal simulation technology is proposed. The verification device can receive the emission signal of MTRV and process the signal by echo simulation technology, including target generation, Doppler frequency shift, time delay, and angel control, and targets are simulated with nominal velocity, distance, and angle value. The processed echo signal with simulated nominal parameter values is reflected to the MTRV. After the echo signal is received and processed by MTRV, the measurement values of simulated velocity, distance, and angle for targets are obtained. Comparing the measured values of the MTRV with the simulated nominal values of the verification device, the measurement error of MTRV is obtained. The verification device of MTRV is realized to verify the accuracy and reliability of the MTRV measurement results. The simulated velocity range of the verification device is up to (-300~300) km/h, and the simulated distance range of the verification device is up to (10~45) m when the simulated incident angle range was within the range of (-60~60)°. The simulation target generation for the two targets of the device is also verified. And the maximum permissible error (MPE) of the simulated velocity was ±0.05 km/h, the MPE of simulated distance is ±0.3 m, and the MPE of simulated angle is ±0.2°. Finally, the verification and uncertainty evaluation results of the MTRV sample validated the effectiveness and feasibility of the proposed verification method and the developed verification device of MTRV.

Uncertainty Quantification of Input Parameters in a 2-D Finite-Element Model for Broken Rotor Bar in an Induction Machine

In this article, a forward uncertainty propagation method is presented for a 2-D finite-element (FE) model in an induction machine. This method is applied to quantify the uncertainty of input parameters, for example, dimensions and material properties, and demonstrate their variability effect on harmonics related to the broken rotor bar (BRB) faults. To show the most influential input parameters in the case of BRB harmonics, a global sensitivity analysis is performed from the polynomial chaos expansion (PCE) approximation of the FE model. The results of this study indicate that BRB harmonics are highly sensitive to stator inner diameter, rotor outer diameter, rotor bar conductivity, and core materials. Moreover, the combined variability of these sensitive input parameters can attenuate the amplitude of the BRB harmonics 30%–90% compared to the simulation results at nominal values of input parameters and closely match with measurement results.

Design of a CMOS memristor emulator-based, self-adaptive spiking analog-to-digital data conversion as the lowest level of a self-x hierarchy

Abstract. The number of sensors used in modern devices is rapidly increasing, and the interaction with sensors demands analog-to-digital data conversion (ADC). A conventional ADC in leading-edge technologies faces many issues due to signal swings, manufacturing deviations, noise, etc. Designers of ADCs are moving to the time domain and digital designs techniques to deal with these issues. This work pursues a novel self-adaptive spiking neural ADC (SN-ADC) design with promising features, e.g., technology scaling issues, low-voltage operation, low power, and noise-robust conditioning. The SN-ADC uses spike time to carry the information. Therefore, it can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things (IoT) and Industry 4.0. We have designed the main part of SN-ADC, which is an adaptive spike-to-digital converter (ASDC). The ASDC is based on a self-adaptive complementary metal–oxide–semiconductor (CMOS) memristor. It mimics the functionality of biological synapses, long-term plasticity, and short-term plasticity. The key advantage of our design is the entirely local unsupervised adaptation scheme. The adaptation scheme consists of two hierarchical layers; the first layer is self-adapted, and the second layer is manually treated in this work. In our previous work, the adaptation process is based on 96 variables. Therefore, it requires considerable adaptation time to correct the synapses' weight. This paper proposes a novel self-adaptive scheme to reduce the number of variables to only four and has better adaptation capability with less delay time than our previous implementation. The maximum adaptation times of our previous work and this work are 15 h and 27 min vs. 1 min and 47.3 s. The current winner-take-all (WTA) circuits have issues, a high-cost design, and no identifying the close spikes. Therefore, a novel WTA circuit with memory is proposed. It used 352 transistors for 16 inputs and can process spikes with a minimum time difference of 3 ns. The ASDC has been tested under static and dynamic variations. The nominal values of the SN-ADC parameters' number of missing codes (NOMCs), integral non-linearity (INL), and differential non-linearity (DNL) are no missing code, 0.4 and 0.22 LSB, respectively, where LSB stands for the least significant bit. However, these values are degraded due to the dynamic and static deviation with maximum simulated change equal to 0.88 and 4 LSB and 6 codes for DNL, INL, and NOMC, respectively. The adaptation resets the SN-ADC parameters to the nominal values. The proposed ASDC is designed using X-FAB 0.35 µm CMOS technology and Cadence tools.

RESEARCH OF PHYSICAL PROCESSES IN LAMINATED MAGNETIC CORES OF ELECTRIC MACHINES

The aim of this work is to use fast-moving processes to detect defects in the inter-sheet insulation of laminated magnetic core of electric machines. Violation of the sheet insulation causes increased eddy currents as a result of increased losses and integrated local overheating in the body of the magnetic core. The article develops a mathematical field model of induction distribution in a toroidal toothed magnetic core of an asynchronous motor series 4AA63V4U3 0.37 kW when superimposed on the back of the core of the power winding powered by a high frequency voltage source in the range. An experimental study of losses in the magnetic coret and the distribution of losses on eddy currents and remagnetization (hysteresis).&#x0D; When designing an electric machine, its operating characteristics, optimal operating modes, thermal condition and many other factors are calculated using the nominal values ​​of steel parameters, windings and all materials used in the machine. However, these materials do not always have the stated characteristics and quality. For example, during the manufacture and stamping of electrical steel sheets, it is exposed to a significant level of influence, which in some way affects its characteristics. In addition, even if we assume that during the production of all stages of manufacturing the material came flawlessly, the condition and as a result the parameters of materials and EM in general change during operation as a result of accidents or even simple aging and wear.&#x0D; Therefore, given these facts, it becomes clear that during planned or unplanned repairs it makes sense to check the condition of materials, insulation, as their condition depends on the allowable loads, temperature, etc. In particular, the state of the magnetic circuit largely determines the temperature around the conductors in the grooves and as a result determines how long the winding will actually last in contrast to the specified service life and rated power at which to use this EM.&#x0D; The concept of the state of the magnetic circuit can be divided into the state of electrical steel and the state of its insulation. The first component changes quite poorly during operation and is generally caused by the "aging" of steel if you do not take into account any serious damage as a result of accidents, but it can be damaged during manufacture. But the second component is significantly affected during operation and significantly determines the quality of the magnetic circuit as a whole.