Search Results (624)

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems. Full article

Existing camera calibration methods using a single image have exhibited some limitations. These limitations include relying on large datasets, using inconveniently prepared calibration objects instead of commonly used planar patterns such as checkerboards, and requiring further improvement in accuracy. To address these issues, a high-quality and convenient camera calibration method is proposed, which only requires a single image of the commonly used planar checkerboard pattern. In the proposed method, a nonlinear objective function is derived by leveraging the linear distribution characteristics exhibited among corners. An algorithm based on enumeration theory is designed to minimize this function. It calibrates the first two radial distortion coefficients and principal points. The focal length and extrinsic parameters are linearly calibrated from the constraints provided by the linear projection model and the unit orthogonality of the rotation matrix. Additionally, a guideline is explored through theoretical analysis and numerical simulation to ensure calibration quality. The quality of the proposed method is evaluated by both simulated and real experiments, demonstrating its comparability with the well-known multi-image-based method and its superiority over advanced single-image-based methods. Full article

Low-power portable devices play a major role in IoT applications, where the analog-to-digital converters (ADCs) are very important components for the processing of analog signals. In this paper, a 5-bit two-step flash ADC with a low-power double-tail dynamic comparator (DTDC) using the control switching technique is presented. The most significant bit (MSB) in the proposed design is produced by only one low-power DTDC in the first stage, and the remaining bits are generated by the flash ADC in the second stage with the help of an auto-control circuit. A control circuit produced reference voltages with respect to the control input and mid-point voltage (${\mathrm{V}}_k$). The proposed design and simulations are carried out using 90 nm CMOS technology. The result shows that the peak differential non-linearity (DNL) and integral non-linearity (INL) are +0.60/−0.69 and +0.66/−0.40 LSB, respectively. The signal-to-noise and distortion ratio (SNDR) for an input signal having a frequency of 1.75 MHz is found to be 30.31 dB. The total power consumption of the proposed design is significantly reduced, which is 439.178 $\mathsf{\mu }$W for a supply voltage of 1.2 V. The figure of merit (FOM) is about 0.054 pJ/conversion step at 250 MS/s. The present design provides low power consumption and occupies less area compared to the existing works. Full article

The amplitude modulation of a pump field and the phase-sensitive detection of a pump-induced intensity change of a probe field encompass a common practice in nonlinear spectroscopies to enhance the detection sensitivity. A drawback of this approach arises when the modulation frequency is comparable to the width of the spectral feature of interest, since the presence of sidebands in the amplitude-modulated pump field provides distortion to the observed spectral lineshape. This represents a problem when accurate measurements of spectral lineshapes and line positions are pursued, as recently happened in our group with the metrology of the Q(1) line in the 1-0 band of molecular hydrogen. The measurement was performed with a Stimulated Raman Scattering spectrometer that was calibrated, for the first time, against an optical frequency comb. In this work, we develop an analytical tool for nonlinear Stimulated Raman spectroscopies that allows us to precisely quantify spectral distortions arising from high-frequency amplitude modulation in one of the interacting fields. Once they are known, spectral distortions can be deconvolved from the measured spectra to retrieve unbiased data. The application of this tool to the measured spectra is discussed. Full article

This paper proposes a Composition Piecewise Memory Polynomial (CPMP) digital predistortion model based on a Vector Switched (VS) behavioral model to address the challenges of severe nonlinearity and strong memory effects in wideband power amplifiers (PAs). To tackle this issue, two thresholds are calculated and used to segment the envelope values of the input signal according to the nonlinear distortion characteristics of the PA. In this approach, a Generalized Memory Polynomial (GMP) model is employed for the lower segment, a Memory Polynomial (MP) model is employed for the middle segment, and a higher-order GMP model is employed for the upper segment. By sharing the fundamental MP among the proposed segmented models and leveraging a design methodology that configures different cross terms, memory depths, and polynomial orders for each segment, this model achieves superior linearization performance while simultaneously reducing the computational complexity associated with model extraction. The experimental results demonstrate that the adjacent channel power ratio (ACPR) of the predistorted PA output signal using the proposed model improves from −36 dBc to −54 dBc, matching the performance of the GMP model. Furthermore, this performance is 0.5 dBc better than the Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models. Notably, the complexity of the proposed parameter extraction process is 28.8% of the DVR model, 21.79% of the GMP model, and 12.83% of the PDDR model. Full article

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model’s maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments. Full article

In this paper, we explore the enhancement of a 4.6 km dual-polarization 2 × 2 MIMO D-band photonic-assisted terahertz communication system using iterative pruning-based deep neural network (DNN) nonlinear equalization techniques. The system employs advanced digital signal processing (DSP) methods, including down-conversion, resampling, matched filtering, and various equalization algorithms to combat signal distortions. We demonstrate the effectiveness of DNN and iterative pruning techniques in significantly reducing bit error rates (BERs) across a range of symbol rates (10 Gbaud to 30 Gbaud) and polarization states (vertical and horizontal). Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. After pruning, the BER at different transmission rates was reduced to below the hard decision forward error correction (HD-FEC) threshold, indicating a substantial improvement in signal quality. Additionally, the pruning process contributed to a decrease in network complexity, with a maximum reduction of 85.9% for 10 GBaud signals and 63.0% for 30 GBaud signals. These findings indicate the potential of DNN and pruning techniques to enhance the performance and efficiency of terahertz communication systems, providing valuable insights for future high-capacity, long-distance wireless networks. Full article

In this paper, we present a disturbance observer-based point of common connection voltage-modulated direct power control (PCCVM-DPC) system, which increases the robustness of the PCCVM-DPC system. First, the mathematical analysis of the disturbances for the step-up transformer’s nonlinearity, the grid voltage harmonics, and the parameter uncertainties is presented. By analyzing the disturbance terms of the PCCVM-DPC system, we present the disturbance observer (DOB) for the PCCVM-DPC system. To assess the efficacy of our approach, we perform comparative studies of the PCCVM-DPC without DOB and PCCVM-DPC with DOB by constructing the simulation environment based on the commercial step-up transformer and ESS inverter datasheet. We have validated that the active and reactive power control performance of the PCCVM-DPC with DOB outperforms the PCCVM-DPC without DOB from the observation that the current total harmonic distortion reduced by more than 40% compared to the PCCVM-DPC without the DOB. Full article

We numerically demonstrate the dispersion map configured by random-based residual dispersion per span (RDPS) applied into the mid-span spectral inversion (MSSI) system to mitigate the impact of chromatic dispersion and the fiber nonlinearity in wavelength division multiplexed (WDM) signals. The dispersion map proposed was a scheme in which the RDPS of all optical fiber spans in the front section of the midway optical phase conjugator (OPC) are randomly selected, and the arrangement order of the RDPS in the rear section is inverted from that of the front section. Numerical simulations were performed by evaluating the compensation of the distorted 960 Gb/s WDM signal as a function of the variation of the DCF length and the SMF length, which are involved in determining RDPS. It was confirmed that the compensation effect of the proposed dispersion maps has improved compared to the conventional dispersion map since the dispersion maps examined in this paper have antipodal symmetry around the midway OPC. In particular, it was confirmed that the method of randomly determining the RDPSs by varying the DCF length slightly improved system performance compared to the variation of SMF lengths. We also found that the feature of the RDPS random distribution patterns can achieve excellent compensation for the distorted WDM signal through 50 iterations. Full article

This paper addresses the challenges and opportunities associated with integrating grid-forming inverters (GFMs) into modern power systems, particularly in the presence of nonlinear loads. Nonlinear loads introduce significant harmonic distortions in the source voltage and current, leading to reduced power factor, increased losses, and an overall reduction in system performance. To mitigate these adverse effects, active filters are employed. The objective of this study is to investigate a synergistic approach to modeling and control in integrated power systems with GFMs, focusing on enhancing power quality and grid stability by reducing harmonic distortions through the use of voltage-source active filters. This research contributes to sustainability by supporting the reliable and efficient integration of renewable energy sources, thereby reducing dependency on fossil fuels and minimizing greenhouse gas emissions. Additionally, improving power quality and system efficiency helps reduce energy waste, which is crucial for achieving sustainable energy goals. Simulations are conducted on a 1000 kW GFM connected to a grid with a nonlinear variable load, demonstrating the system’s effectiveness in adapting to dynamic conditions, reducing harmonics, and promoting a stable, resilient, and sustainable power grid. Full article

The availability of synthetic indicators of the degree and type of nonlinearity in systems is used in various fields to assess system quality or to highlight possible malfunctions. Different distortion or damage indexes are synthetic measures designed (and standardized) to evaluate the frequency trend of specific aspects resulting from the nonlinear behavior of the system under consideration. The different measures of deviation from linear behavior quantitatively consider the system and its nonlinearity characteristics; they were defined according to practically feasible measurement methodologies and the various aspects of the system’s nonlinearity that needed to be highlighted. In parallel, techniques for representing and modeling nonlinear systems have been defined, capable of describing the system in a more general way, attempting to capture its input–output characteristics by varying the level of stress to which the system is subjected. Numerous modeling techniques have been proposed, aimed at representing the nonlinear behavior of physical devices. In this paper, after an extensive description of the Hammerstein model identification technique based on swept sinusoidal signals, we show how the nonlinear model of the system can be used to obtain accurate estimates of the parameter aimed at describing the nonlinearity characteristics of the system. This extensive description makes it possible to point out that the same Hammerstein model can be obtained not only from a single type of excitation, but it is shown that the identification technique can be extended to input signals of different types. The description of the method also makes clear the motivation behind the introduction of the proposed original technique for estimating, from a single measurement, the model parameters of the nonlinear system—and from these the synthetic estimators—relative to multiple values of the input signal amplitude, thus enabling a considerable increase in the estimation efficiency of these parameters. The proposed technique is verified with both synthetic and laboratory experiments, demonstrating the effectiveness of the method in evaluating nonlinear system parameters, distortion estimates, and parameters defined for an early detection of defects of the structure. Full article

In this research, we investigate the power quality of the grid where an Electric Arc Furnace (EAF) with a very high load operates. An Electric Arc Furnace (EAF) is a highly nonlinear load that uses very high and variable currents, causing major power quality issues such as voltage sags, flickers, and harmonic distortions. These disturbances produce electrical grid instability, affect the operation of other equipment, and require strong mitigation measures to reduce their impact. To investigate these issues, data are collected from the Point of Common Coupling where the Electric Arc Furnace is fed. The following three main factors are identified for evaluating power quality: apparent power, active and reactive power, and distorted power. Along with these powers, Total Harmonic Distortion, an important indicator of power quality, is calculated. These data are collected during the full process of producing a complete steel batch. To create a Deep Neural Network that can model and forecast power quality parameters, a network is developed using LSTM layers, Convolutional Layers, and GRU Layers, all of which demonstrate good prediction performance. The results of the prediction models are examined, as well as the primary metrics characterizing the prediction, using the following: MAE, RMSE, R-squared, and sMAPE. Predicting active and reactive power and Total Harmonic Distortion (THD) proves useful for anticipating power quality problems in an Electric Arc Furnace (EAF). By reducing the EAF’s impact on the power system, accurate predictions will anticipate and minimize disturbances, optimize energy consumption, and improve grid stability. This research’s principal scientific contribution is the development of a hybrid deep neural network that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers. This deep neural network was designed to predict power quality metrics, including active power, reactive power, distortion power, and Total Harmonic Distortion (THD). The proposed methodology indicates an important step in improving the accuracy of power quality forecasting for Electric Arc Furnaces (EAFs). The hybrid model’s ability for analyzing both time-series data and complex nonlinear patterns improves its predictive accuracy compared to traditional methods. Full article

This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high-power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different phase relationships between the linear and nonlinear branches, suggesting independent gain and phase expansion characteristics with this topology. This model is used to assess the impact of diode resistance, capacitance, and ideality factors on the APD characteristics. This feature is showcased with two similar GaAs diodes to find the best fit for the considered HPA. The selected diode is characterized and modeled between 1 and 26.5 GHz. A comprehensive APD design and simulation workflow is reported. Before fabrication, the simulated APD is evaluated with the measured HPA to verify linearity improvements. The APD prototype achieves a large-signal bandwidth of 6 GHz with 3 dB gain expansion and 8° phase rotation. This linearizer is demonstrated with a 17–21 GHz GaN HPA with 41 dBm output power and 35% efficiency. Using a wideband 750 MHz signal, this APD improves the noise–power ratio (NPR) by 6.5–8.2 dB over the whole HPA bandwidth. Next, the HPA output power is swept to compare APD vs. power backoff for the same NPR. APD improves the HPA output power by 1–2 W and efficiency by approximately 5–9% at 19 GHz. This efficiency improvement decreases by only 1–2% when including the APD post-amplifier consumption, thus suggesting overall efficiency and output power improvements with APD at K-band frequencies. Full article

In this essay, I recover queer Indigenous P’urhépecha histories in Michoacán, México, by claiming queer P’urhépecha research methods. To do so, I introduce the Indigenous methodology of talking-while-walking, which refers to how I learned to connect with P’urhépecha knowledge and traditions through the voice of my P’urhépecha grandfather. Since the colonial system eradicated queer histories from my land, I seek historical narratives about queer people in Michoacán from any source available to me, including oral histories, archives, information in the media, and interviews. I argue that queer P’urhépecha histories are unstable and non-linear, and that P’urhépecha bodies have been hunted and their histories distorted, provoking fear and false speculations about queerness among the collective. I also examine the attachment of P’urhépecha people to gender binary traditions and heteronormativity and how the narratives behind these practices relate to colonial violence and the persecution of queer P’urhépechas. Thus, I demonstrate how P’urhépecha queerness has been marginalized and simultaneously displaced from the archival records while I claim queer P’urhépecha histories and build queer P’urhépecha archives. Finally, I propose a sensitive and personal approach to queer histories guided by the voices of my queer P’urhépecha interlocutors and the histories that my P’urhépecha abuelo passed to me. Full article

The effects of harmonics, interharmonics, and subharmonics on low-voltage distribution networks, leading to a deterioration in electrical power quality, have become more evident in recent years. The main harmonic sources are power electronic devices due to their implicit nonlinearity. Interharmonic and subharmonic components are mainly caused by a lack of synchronization between the grid frequency and the switching frequency of the power converters. This can be caused by asynchronous modulated devices, or more commonly by fluctuations in the fundamental grid frequency. Interharmonic currents cause interharmonic voltage distortions that affect grid-synchronized or frequency-dependent systems. The IEC-61000-4-7 proposes a general guide on harmonics, interharmonic measurements, and instrumentation in current supply systems. However, the techniques proposed in the standard are intended for measurement and do not enable a precise identification of the interharmonic components in a signal. This work proposes new definitions for the spectral energy aggrupation to improve signal component detection for the IEC standard. Furthermore, an adaptive Kalman filter algorithm is developed that enables the exact identification in real time of the frequency, amplitude, and phase of these components. The proposed system will become the basis for the implementation of a new range of measurement systems that provide improved accuracy and real-time operation. The work is supported by simulated results analysing various scenarios (including transients after changes in harmonic content in the grid voltage) that demonstrate the effectiveness of the proposed method. Full article