Inverted Model Research Articles

High-Temperature Polymer Dielectrics Designed Using an Invertible Molecular Graph Generative Model.

Generating new molecules with the desired physical or chemical properties is the key challenge of computational material design. Deep learning techniques are being actively applied in the field of data-driven material informatics and provide a promising way to accelerate the discovery of innovative materials. In this work, we utilize an invertible graph generative model to generate hypothetical promising high-temperature polymer dielectrics. A molecular graph generative model based on the invertible normalizing flow is trained on a data set containing 250k polymer molecular graphs (mostly generated by an RNN-based generative model) to learn the invertible transformations between latent distributions and molecular graph structures. When generating molecular graphs, a sample vector is drawn from the latent space, and then an adjacency tensor and node attribute matrix are generated through two invertible flows in two steps and assembled into a molecular graph. The model has the merits of exact likelihood training and an efficient one-shot generation process. The learned latent space is used to generate polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) for the application of high-temperature energy storage film capacitors. This work contributes to the efficient design of high-temperature polymer dielectrics by using deep generative models.

Magnetic Inversion through a Modified Adaptive Differential Evolution

In recent decades, differential evolution (DE) has been employed to address a diverse range of nonlinear, nondifferentiable, and nonconvex optimization problems. In this study, we introduce an enhanced adaptive differential evolution algorithm to address the inversion problem associated with magnetic data. The primary objective of the inversion process is to minimize the discrepancy between observed data and predicted data derived from the inverted model. So, the contributions of this paper include the following two points. First, a new mechanism for generating crossover rate (CR) is proposed, which tends to reduce the CR values corresponding to vectors with better objective function values. Second, a new mutation strategy with direction information is proposed to expedite convergence. Additionally, modifications were made to the adjustment of the regularization factor to prevent it from becoming too minimal, thereby preserving its efficacy. The proposed algorithm is validated through synthetic models and a field example. Results from synthetic models demonstrate that our method is superior to and competitive with the original adaptive DE in both solution quality and convergence velocity. For the field example, the Inverted models align closely with the drill-well information.

An attempt to use convolutional neural network to recover layered-earth structure from electrical resistivity tomography survey

The Electrical Resistivity Tomography (ERT) method is a geophysical prospecting technique for determining subsurface structure in terms of electrical resistivity distribution. Direct current is injected into the ground through a pair of electrodes, and the potential difference is measured using another pair of electrodes. The estimated resistivity model of the subsurface is then obtained using a mathematical optimization algorithm called the inversion algorithm, which incorporates multiple measured potential differences from different electrode configurations as the input data. The inverted resistivity model serves as a resource for the interpretation process of the survey. Specialists employ the model and the prior geological information in the area to extract the subsurface geological structure under the data measurement profile. The inverted model can be treated as a two-dimensional image in which each element represents resistivity. The image can be visualized as a blurred or distorted version of the actual resistivity image of the subsurface. We, therefore, developed a post-inversion image enhancement code using a neural network aiming to recover the true subsurface model with predetermined features. We employed the convolutional neural network (CNN) that uses the inverted models as input and returns an enhanced model as output. We tested our developed neural network with a case study that utilized the proposed method to recover the layered earth structure which is often found in real surveys. The electrode spacing was 5 meters in the Schlumberger array configuration. The true subsurface resistivity models of training data and validating data for evaluating the neural network were randomly generated based on the predetermined feature of the layered structure. We then generated their respective inverted models from those data through simulated inversion routine. As a result, 94.75% of the enhanced models in the validation dataset exhibited a lower loss metric compared to the original inverted models in the same validation dataset. We also applied the proposed method to practical field data to recover the subsurface layers and compared them with stratigraphic information from a borehole in each area. The result showed that the proposed method gives well-resolved structures in the enhancement process after the typical inversion.

THD analysis and small AC signal analysis of trans-Z-source and quasi-Z-source inverter for linear and non-linear load

The paper primarily focuses on the ongoing research related to Z source inverter technologies (ZSITs) used in the transportation industry, particularly for electric vehicles (EVs) and hybrid electrical vehicles (HEVs). It analyzes various topologies of ZSITs based on their mode of operation, capacitor voltage stress during active states and non-active state operations, DC link voltage before inverter module and line to line output voltage across the load. The paper compares the conventional trans-z source and two port quasi-Z source inverter model networks for multiple parameters and derives mathematical equations for both linear and non-linear loads. Practical and theoretical calculations match when using a modulation index of M=0.85 and a duty ratio of 1.414. The result obtained in this paper indicates that the proposed solution is better in terms of reduction in total total harmonics distortions (THD) percentage of 18.14% during the transition condition. All the analysis has been carried out with MATLAB based Simulink model.

Uncovering a CF3 Effect on X-ray Absorption Energies of [Cu(CF3 )4 ]- and Related Copper Compounds by Using Resonant Diffraction Anomalous Fine Structure (DAFS) Measurements.

Understanding the electronic structures of high-valent metal complexes aids the advancement of metal-catalyzed cross coupling methodologies. A prototypical complex with formally high valency is [Cu(CF3 )4 ]- (1), which has a formal Cu(III) oxidation state but whose physical analysis has led some to a Cu(I) assignment in an inverted ligand field model. Recent examinations of 1 by X-ray spectroscopies have led previous authors to contradictory conclusions, motivating the re-examination of its X-ray absorption profile here by a complementary method, resonant diffraction anomalous fine structure (DAFS). From analysis of DAFS measurements for a series of seven mononuclear Cu complexes including 1, here it is shown that there is a systematic trifluoromethyl effect on X-ray absorption that blue shifts the resonant Cu K-edge energy by 2-3 eV per CF3 , completely accounting for observed changes in DAFS profiles between formally Cu(III) complexes like 1 and formally Cu(I) complexes like (Ph3 P)3 CuCF3 (3). Thus, in agreement with the inverted ligand field model, the data presented herein imply that 1 is best described as containing a Cu(I) ion with dn count approaching 10.

Uncovering a CF3 Effect on X‐ray Absorption Energies of [Cu(CF3)4]− and Related Copper Compounds by Using Resonant Diffraction Anomalous Fine Structure (DAFS) Measurements**

AbstractUnderstanding the electronic structures of high‐valent metal complexes aids the advancement of metal‐catalyzed cross coupling methodologies. A prototypical complex with formally high valency is [Cu(CF3)4]− (1), which has a formal Cu(III) oxidation state but whose physical analysis has led some to a Cu(I) assignment in an inverted ligand field model. Recent examinations of 1 by X‐ray spectroscopies have led previous authors to contradictory conclusions, motivating the re‐examination of its X‐ray absorption profile here by a complementary method, resonant diffraction anomalous fine structure (DAFS). From analysis of DAFS measurements for a series of seven mononuclear Cu complexes including 1, here it is shown that there is a systematic trifluoromethyl effect on X‐ray absorption that blue shifts the resonant Cu K‐edge energy by 2–3 eV per CF3, completely accounting for observed changes in DAFS profiles between formally Cu(III) complexes like 1 and formally Cu(I) complexes like (Ph3P)3CuCF3 (3). Thus, in agreement with the inverted ligand field model, the data presented herein imply that 1 is best described as containing a Cu(I) ion with dn count approaching 10.

A Comparative Study between Reduced-Order and Switching Models of Grid-Following Inverter Interfacing Renewable Energy Sources

This paper presents a performance comparison between switching and reduced-order models of grid-following inverters. Different models have been developed to study the inverter’s operation using MATLAB and PSIM simulation tools. These models offer a tradeoff between accuracy and computational burden. The switching model fully represents the discontinuities in the inverter due to switching, but such a model demands high computational resources. The inverter representation is simplified using reduced-order models, which is common in the literature. However, the fidelity of these simplified models might not be adequate for some applications. Therefore, it is essential to investigate the performance of such models closely to ensure their suitability depending on the purpose of the analysis. Hence, a comparative study between switching and reduced-order models is presented here by simulating different scenarios and operation conditions. The results have validated the efficacy of the reduced-order model in representing the grid-following inverter’s operation in both steady state and response to a step change resulting from active or reactive power variation. Unlike the switching model, the findings illustrate that the reduced-order modeling fails to represent harmonics and losses. After simulating different scenarios, this work highlights the main differences between the switching and reduced-order models to facilitate the choice of the proper modeling method for researchers.

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2=0.99, RMSE=0.61) and measured dataset (R2=0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Constructing a 3‐D Radially Anisotropic Crustal Velocity Model for Oklahoma Using Full Waveform Inversion

AbstractOver the past decade, the seismicity rate in the state of Oklahoma has increased significantly, which has been linked to industrial operations, such as saltwater injection and hydraulic fracturing. Taking advantage of induced earthquakes and recently deployed seismometers, we construct a 3‐D radially anisotropic seismic velocity model for the crust of Oklahoma by using full waveform inversion. To mitigate the well‐known cycle‐skipping problem, we use misfit functions based on phase and waveform differences in several frequency bands. Relative velocity perturbations in the inverted model allow us to delineate major geological provinces in Oklahoma, such as the Anadarko Basin and the Cherokee Platform/Shelf. In addition, radial anisotropy in the inverted model reflects deformation within the crust of Oklahoma, which might correlate with sedimentary layering, microcracks/fractures, as well as dominant orientation of anisotropic minerals. The crystalline basement beneath Oklahoma can be inferred from the new velocity model, which enables us to better classify induced seismicity in current earthquake catalogs. Furthermore, synthetic experiments suggest that the new velocity model enables us to better constrain earthquake locations in Oklahoma, especially for determining their depths, which are important for investigating induced seismicity.

Reduced-Order Modeling of Droop-Controlled Inverters Using Slow Coherency and Aggregation Algorithm

By considering time-scale separation characteristics and nonlinear node voltage equation, an improved slow coherency algorithm has been proposed, and for the first time, applied to the slow coherency model widely used for modeling three-phase droop-controlled grid-connected inverters. The method works by combining two coherency clustering results, which in turn, are computed from mode matrix, representing slow mode and damping coefficient of the slow coherency model. Subsequently, fuzzy C-means spatial clustering algorithm and aggregation method are used for identifying coherent generators and building the reduced-order model, respectively. Compared to the traditional slow coherency algorithms, the presented method can accurately identify oscillating mode between power supplies under the condition of selecting any number of areas. Its range of application can moreover be expanded to cover distributed networks with multiple droop-controlled inverters. These expectations, and accuracy and effectiveness of the proposed method have eventually been verified by simulations performed with both detailed and reduced-order models under different faults.

Prediction of variation aware FOSC in ring oscillators (ROs) to mitigate the impact of aging on RO-PUF

We propose a methodology to predict device-level variability (including aging) impact on the oscillation frequency (Fosc) of an N-stage ring oscillator (RO). This task is accomplished by creating a tool in a Python environment that uses our own developed variability-aware timing models of the CMOS inverter. Moreover, we use the model to foretell the impact of aging on the logical effort (g) of a CMOS inverter. Using the modified g, we resize ROs in an RO-based physical unclonable function (PUF) in the pre-layout stage to mitigate the impact of aging on the reliability of RO-PUF. The simulation is performed in the Cadence AMS environment using STMicroelectronics (STM) 28 nm CMOS process technology. With a one-time SPICE/aging simulation, the proposed methodology eliminates SPICE/aging simulation overhead for the prediction of variability impact on Fosc of a given N-stage RO. This approach mitigates the impact of aging on the reliability of RO-PUF and provides a method for variability (including aging) aware design in the pre-layout stage.

Blackbox Small-Signal Modeling of Grid-Connected Inverters in Asymmetrical Power Grids

Blackbox Small-Signal Modeling of Grid-Connected Inverters in Asymmetrical Power Grids

Influence of switching sequences on the neutral point voltage balance in a three-level voltage source invertor

BACKGROUND:Ensuring the voltage balance of the neutral point of the DC link within acceptable limits is one of the mandatory requirements during the operation of a three-level autonomous neutral point clamped voltage source inverter. It is known that neutral point voltage imbalance can adversely affect the operation of the inverter and the load at all, lead to the failure of both power switches and capacitors in the DC link. Neutral point voltage imbalance mostly often occurs due to the asymmetry of the nominal values of DC capacitors, inconsistent properties of switching devices, asymmetric three-phase load, and also due to the imperfection of the converter control algorithm.
 AIMS:Selection of the optimal switching sequence that ensures the smallest deviation of the neutral point voltage and the number of switching of power switches in a three-level voltage source inverter.
 METHODS:For an objective comparison of the considered switching sequences and their influence on the neutral point voltage balance, a simulation model of a three-level neutral point clamped voltage source inverter was developed in theMATLAB/Simulinksoftware. To control this inverter, a space vector modulation method was used with three different switching sequences: five-segment, seven-segment and standard.
 RESULTS:The topology of a three-level neutral point clamped voltage source inverter is presented. The experimental dependencies of the neutral point voltage deviation error was taken with a change in the modulation coefficient, the frequency of the fundamental harmonic at the inverter output, and load characteristics for three different switching sequences.
 CONCLUSIONS:In this paper, the influence of the basic vectors and the influence of switching sequences on the neutral point voltage balance are studied. Combinations of states of large and zero basic vectors do not affect the neutral point voltage. For the medium basic vectors, the neutral point voltage can increase or decrease depending on the operating conditions of the inverter. The small basic vectors significantly affect the neutral point voltage. Considering the abovementioned, the seven-segment sequence that ensures both the optimal balance of the neutral point voltage and the level of switching losses should be considered as the best switching sequence for a three-level inverter.

Simulation of autonomous voltage inverters of power sources of single-phase asynchronous drives

Purpose. Development of original models of voltage inverters adapted to control a single-phase asynchronous motor with the subsequent prospect of creating automatic systems for improving the energy efficiency of household mechanisms, as well as developing educational complexes for studying the operation of frequency converters to form skills for their repair and tuning.Methodology. The provisions of the theory of electric machines, methods of synthesis of digital control systems, methods and methods of modeling in CAD electronic laboratory of the company NATIONAL INSTRUMENTS of the Multisim component, methods of circuit design were used for conducting research. Findings. Models of pulse distributors were developed and built for the cases of power supply of single-phase asynchronous motors with bridge inverters of separate and individual starting and working windings, half-bridge inverters with a bipolar power source and a three-phase inverter with control according to a 180-degree control diagram. Time diagrams of visual confirmation of the system's performance according to the declared principles have been obtained. Originality. A number of original switch diagrams of the power section of the models of impulse distributors of autonomous voltage inverters are proposed, taking into account the phase shift and the duration of the switching pauses of the switching arms of the power unit to avoid emergency modes, recommendations are given on the levels of generation frequencies that are fed to the input of the combinational circuit, the prospect of using this system as part of a single-phase asynchronous electric drive with the possibility of energy-efficient control is shown.Practical value. The proposed original model solutions for autonomous voltage inverters, which are adapted to work together with a single-phase IM, can be useful for developers of converter technology and become visual material for their use in the educational process for training specialists in specialty 141 "Electric power engineering, electrical engineering and electromechanics".

Generative AI for performance-based design of engineered cementitious composite

Engineered cementitious composite (ECC) has been intensively studied due to its excellent tensile performance. However, classical micro-mechanical design theory of ECC is qualitative and fails to give detailed ECC mixtures at specific tensile parameters. This study aims to develop a performance-based mixture design model to generate ECC mixtures using generative AI method. An experimental database consisting of 129 polyethylene fiber reinforced ECC (PE-ECC) records has been built. The database was used to train one invertible neural network model and two artificial neural network models. A series of PE-ECC mixtures were generated by the proposed model based on desired mechanical performance and sustainable requirements. Based on the experimental results, the developed model was proven to compose PE-ECC mixtures that satisfy the target requirements with a maximum deviation of less than 16%. The neural network-based model can be used in various application scenarios (e.g., low-cost ECC and low-carbon ECC), thus promoting the development of ECC materials in the area of research and engineering application.

Particle Swarm Optimization (PSO) of High-Quality Magnetic Data of the Obudu Basement Complex, Nigeria

The particle swamp optimization procedure was applied to high-quality magnetic data acquired from the Precambrian Obudu basement complex in Nigeria with the object of estimating the distinctive body parameters (depth (z), index angle (θ), amplitude coefficient (K), shape factor (Sf), and location of the origin (x0)) of magnetic models. The magnetic models were obtained from four profiles that ran perpendicular to the observed magnetic anomalies within the study area. Profile A–A’ with a length of 2600 m is characterized by inverted model parameters of K = 315.67 nT, z = 425.34 m, θ = 43°, Sf = 1.15, and x0 = 1554.86 m, while profile B–B’ with a length of 5600 m is described by K = 257.71 nT, z = 543.75 m, θ = 54°, Sf = 0.96, and x0 = 3645.42 m model parameters. Similarly, profile C–C’ with a length of 3000 m is defined by K = 189.53 nT, z = 560.87 m, θ = 48, Sf = 1.2, and x0 = 1950 m. Profile D–D’, which is well-defined by a 2500 m length, started at the crest of the observed magnetic anomaly and displays inverted model parameters of 247.23 nT, 394.16 m, 39°, 1.26, and 165.41 m. Correlatively, the estimated shape factor of the four models (Sf = 1.15, 0.96, 1.2, and 1.26) shows that the magnetic models are linked to thin sheets. Furthermore, quantitative interpretations of the models show that the PSO operation is rapid and proficient.

Wide frequency band oscillation mechanism and suppression measures for grid-interfaced PV inverters based on impedance analysis

With the rapid development of PV and wind power, the energy structure of the power grid is changing dramatically. The dominant power supply role played by conventional power sources such as hydropower and thermal power is gradually replaced by PV and wind power. All the power electronics-based equipment makes the power system stable form more complex, and one of the most significant challenges is wide frequency band oscillation (WFBO). This paper proposes an impedance analysis method for PV inverters in high proportion to new energy-integrated power systems. First, we investigate WFBO mechanism caused by the PV inverter based on its impedance model, and then a detailed impedance model of parallel PV inverters is developed to study WFBOs in PV stations. Finally, the WFBO suppression measure based on active damping control is proposed. Simulation results prove the correctness and feasibility of the active damping control strategy.

PSO BASED HARMOIC MITIGATION IN SINGLE PHASE SINE WAVE VOLTAGE SOURCE INVERTER

This paper explains the pruning of power harmonics in a single-phase voltage source inverter using PI controller by calculating the optimized values of PI constants by Particle Swarm Optimization (PSO) Algorithm. The mapping state space model of the sine wave inverter was formulated by considering the operating sequence of full bridge sine wave inverter. The controller acts effectively thereby reducing the voltage and current THDs in an inverter. The PSO technique is implemented which estimates the optimized parameters of constant for PI controller thereby tuning the controller for reducing the total harmonics of a full bridge inverter. The MATLAB / SIMULINK tool and experimental verifications are done for harmonic reduction, and it is found that its harmonics were reduced well than IEEE standards with improved transient response.

A Subthreshold Biased CMOS Ring Oscillator Model Design in 180-nm Process

In this paper, a 180-nm CMOS ring oscillator design, made with halo-implanted transistors and operating in the weak inversion region, is proposed, based on an undergraduate integrated circuit design course methodology for building logic gates and comparing simulated results with reviewed literature data. Halo-implanted channel transistors have a steeper and less distorted voltage characteristic curve compared to uniformly doped channel ones, which makes them a more appropriate option when designing asynchronous digital integrated circuits aimed at low bias and low power. Three gate models were created using weak inversion and pull-up and pull-down networks made with halo-implanted transistors. The results of the study and simulation of the three inverter digital gate topologies showed that the NOT inverter model, as expected, had a higher frequency than the NAND and NOR inverter models. The ring oscillators made with the NOT inverter came up with an 8.25-MHz switching frequency as well as a dynamic power close to 270 nW. A comparison with other ring oscillators from previous studies is also shown.

Large-Signal Impedance Modeling and Stability Analysis of a Grid-Connected Inverter Considering the Influence of a Limiter in Different Control Links

The limiter in grid-connected inverter control may cause sustained oscillation in the system. The large-signal impedance model is provided since the traditional small-signal impedance model cannot accurately describe the characteristics of the limiter. In this paper, there are three established large-signal impedance models of grid-connected inverters that take into account the limiter in the phase-locked loop (PLL), the limiter in the current control loop and the limiter in the pulse width modulation (PWM). Based on the established models, the influences of these different limiters on the output impedance characteristics of the grid-connected inverter are discussed. Furthermore, the stability of the system considering the influence of different limiters is analyzed and the oscillation frequency and amplitude of the system are predicted. The simulation verifies the accuracy of the large-signal impedance model and the predicted oscillation frequency and amplitude.