Search Results (654)

Aberrant responding poses a significant challenge in measurement and validity, often distorting well-established relationships between psychological and educational constructs. This study examines how aberrant response patterns influence the relationship between student–teacher relations and students’ perceptions of school safety. Using data from 6617 students from the Saudi Arabia Kingdom from the 2022 Programme for International Student Assessment (PISA), we employed the cusp catastrophe model to evaluate the nonlinear dynamics introduced by aberrant responses, as measured by the U3 person-fit index and the number of Guttman errors. Theoretical and empirical support for the cusp model suggests that aberrance functions as a bifurcation parameter, shifting the relationship between student–teacher relations and perceived school safety from predictable linearity to chaotic instability when exceeding a critical threshold in aberrant responding. Results indicate that both the U3 index and the number of Guttman errors significantly contribute to response distortions, confirming the cusp model’s superiority over traditional linear and logistic alternatives. These findings suggest that ignoring aberrant responding risks misinterpreting data structures, while properly accounting for it through catastrophe models provides a more nuanced understanding of nonlinear system behavior in educational assessment. The study highlights the importance of person-fit statistics in psychometric evaluations and reinforces the predictive utility of nonlinear models in handling response distortions in large-scale assessments. Full article

In Fourier Transform Spectrometers, phase errors in spectral measurement induce distortion in reconstructed spectra. Existing phase correction algorithms demonstrate insufficient precision in addressing both linear phase and instrumental phase components, resulting in limited applications for the restored spectra in the field of precision measurement. This paper proposes an algorithm called the Cross-Teager–Kaiser ${\psi }_B$ Energy Operator–Nonlinear Calibration Model (CTKB-NCM) for phase error correction. The algorithm first uses the cross-Teager–Kaiser ${\psi }_B$ energy operator (CTKB) method to correct linear phase errors, then applies the Nonlinear Calibration Model (NCM) to solve for the instrument phase correction parameters at each wavenumber, and finally uses the instrument phase correction parameters to correct the residual phase after the linear phase error has been corrected. The Rao algorithm is used to determine the optimal instrument phase correction parameters. Simulation experiments demonstrate that the CTKB-NCM method achieves an order-of-magnitude improvement in normalized reconstructed spectral accuracy for SO₂ gas compared to the conventional Mertz method. Full article

Accurate image registration is essential for various remote sensing applications, particularly in multi-temporal image analysis. This paper introduces DVF-NET, a novel deep learning-based framework for dual-temporal remote sensing image registration. DVF-NET integrates two displacement vector fields to address nonlinear distortions caused by significant variations between images, enabling more precise image alignment. A key innovation of this method is the incorporation of a Structural Attention Module (SAT), which enhances the model’s ability to focus on structural features, improving the feature extraction process. Additionally, we propose a novel loss function design that combines multiple similarity metrics, ensuring more comprehensive supervision during training. Experimental results on various remote sensing datasets indicate that the proposed DVF-NET outperforms the existing methods in both accuracy and robustness, particularly when handling images with substantial geometric distortions such as tilted buildings. The results validate the effectiveness of our approach and highlight its potential for various remote sensing tasks, including change detection, land cover classification, and environmental monitoring. DVF-NET provides a promising direction for the advancement of remote sensing image registration techniques, offering both high precision and robustness in complex real-world scenarios. Full article

Land-based financing plays an essential role in urbanization in the developing world, and it is widely recognized to have profound environmental effects. However, there have been relatively few research endeavors on the impact of land finance on vegetation dynamics. This study applies fixed effects models and an instrumental variable approach to examine the impact of land finance on vegetation status and mechanisms of influence, using data for 286 Chinese cities between 2011 and 2022. The nonlinear relationship between land finance and vegetation conditions at different levels of economic development is investigated by estimating panel threshold models. The findings show that land finance exerts an inhibiting impact on vegetation conditions. The restraining effect of land finance on vegetation status tends to be more pronounced in western China or in secondary industry-led cities. The analysis of mechanisms of influence indicates that land finance negatively affects vegetation conditions by speeding up urban expansion, suppressing innovation, reducing land use efficiency, and distorting the fiscal expenditure structure. The analysis of the threshold effect suggests that land finance exerts a stronger curbing effect on vegetation status as the economic development level rises. The findings have significant policy implications for deepening reform of the fiscal system and promoting vegetation protection and restoration. Full article

This paper proposes a low-power 10-bit 20 MS/s pipelined analog-to-digital converter (ADC) designed for the burgeoning needs of low-data-rate communication systems, particularly within the Internet of Things (IoT) domain. To reduce power usage, multiple power-saving techniques are combined, such as sample-and-hold amplifier-less (SHA-less) architecture, capacitor scaling, and dynamic comparators. In addition, this paper presents a novel operational amplifier (op-amp) with gain boosting, featuring a dual-input differential pair that enables internal pipeline stage switching, effectively alleviating the crosstalk and memory effects inherent in conventional shared op-amp configurations, thereby further reducing power consumption. A prototype ADC was fabricated in a 180 nm CMOS process and the core size was 0.333 mm². The ADC implemented operated at a 20 MHz sampling rate under a 1.8 V supply voltage. It achieved a spurious-free dynamic range (SFDR) of 61.83 dB and a signal-to-noise-and-distortion ratio (SNDR) of 54.15 dB while demonstrating a maximum differential non-linearity (DNL) of 0.36 least significant bit (LSB) and a maximum integral non-linearity (INL) of 0.67 LSB. Notably, the ADC consumed less than 5 mW of power at the mentioned sampling frequency, showcasing excellent power efficiency. Full article

Side-scan sonar and multi-beam echo sounder (MBES) are the most widely used underwater surveying tools in marine mapping today. The MBES offers high accuracy in depth measurement but is limited by low imaging resolution due to beam density constraints. Conversely, side-scan sonar provides high-resolution backscatter intensity images but lacks precise positional information and often suffers from distortions. Thus, MBES and side-scan images complement each other in depth accuracy and imaging resolution. To obtain high-quality seafloor topography images in practice, matching between MBES and side-scan images is necessary. However, due to the significant differences in content and resolution between MBES depth images and side-scan backscatter images, they represent a typical example of heterogeneous images, making feature matching difficult with traditional image matching methods. To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. By leveraging self-attention and cross-attention mechanisms, the features of the MBES and side-scan images are combined, and a similarity matrix of the two modalities is calculated to achieve mutual matching. Experimental results show that, compared to traditional methods, the proposed model exhibits greater robustness to noise interference and effectively reduces noise. It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images. Full article

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets. Full article

The quantization process of synthetic aperture radar (SAR) images faces significant challenges due to their high dynamic range, resulting in notable quantization distortion. This not only degrades the visual quality of the quantized images but also severely impacts the accuracy of image interpretation. To mitigate the distortion caused by uniform quantization and enhance visual quality, this paper introduced a novel nonlinear quantization framework via signal-to-noise ratio (SNR) enhancement and segmentation strategy guidance. This framework introduces guiding information to improve quantization performance in weak scattering regions. A histogram adjustment method is developed to incorporate the spatial information of SAR images into the quantization process to enhance the quantization performance, specifically within weak scattering regions. Additionally, the optimal quantizer is improved by refining the SNR distribution across quantization units, addressing imbalances in their allocation. Experimental results based on Gaofen-3 (GF-3) satellite data demonstrate that the proposed algorithm approaches the global quantization performance of optimal quantizers while achieving superior local quantization performance compared to existing methods. Full article

This paper proposes a current harmonic suppression strategy that combines harmonic synchronous rotating frame (HSRF) current feedback control and back-electromotive force harmonic (BEMFH) feedforward compensation to suppress the fifth and seventh current harmonics of a six-phase permanent magnet synchronous motor (PMSM). The current harmonics of six-phase PMSMs vary with the current due to manufacturing imperfections and the inverter nonlinearity effect. Using fixed-parameter BEMFH feedforward compensation cannot completely eliminate current harmonics. This paper integrates a closed-loop harmonic current control strategy, using HSRF in the differential mode of the six-phase PMSM rotor rotating frame to effectively mitigate current harmonic variations caused by load changes. The controller adapts a Texas Instrument microcontroller featuring encoder interfaces, complementary pulse width modulation (PWM), and analog–digital converters (ADC) to simplify the board design. The rotor angle feedback is provided by a 12-pole resolver in conjunction with an Analog Device resolver-to-digital converter (RDC). The specifications of the six-phase PMSM are as follows: 12 poles, 1200 rpm, 200 A (rms), and 600 V DC bus. The total harmonic distortion (THD) of the phase current for harmonics below the 21st order was reduced from 31.71% to 4.84% under the test conditions of 1200 rpm rotor speed and 200 A peak phase current. Specifically, the fifth and seventh harmonics were reduced from 29.98% and 9.72% to 2.74% and 1.21%, respectively. These results validate the feasibility of the proposed current harmonic suppression strategy. Full article

Grid-connected photovoltaic (PV) systems inject nonsinusoidal currents into the grid at the point of their connection. The technology of the inverter utilized for the conversion of DC power into AC is directly associated with distortion characteristics. Even though pulse-width-modulated (PWM) converters generate considerably lower harmonic distortion than their predecessors, they are responsible for the emergence of a new power quality issue in distribution grids known as supraharmonics, which can cause problems such as overheating and malfunctions of equipment. PV systems are known sources of supraharmonics, but their impact has not yet been thoroughly researched. Due to the multitude of parameters affecting their performance, a more rigorous treatment is required compared to more common nonlinear devices. In this paper, emissions from a three-phase grid-connected PV system are examined by means of a dedicated simulation tool taking into account the specifics of inverter switching action without overly increasing computational cost. The impact of environmental parameters as well as factors affecting the switch control of the converter is investigated. The dependence of the supraharmonic emission of the PV system on the converter characteristics rather than environmental conditions is demonstrated. Furthermore, simulation studies on a network comprising the PV system and an additional supraharmonic-emitting system in simultaneous operation are conducted. Their combined effect on the distortion at the connection point of the network to the grid is assessed by means of a power flow-based approach, capable of quantifying interactions within this network. From the viewpoint of the grid, an increase of supraharmonic-related disturbance at low irradiance conditions is revealed. Full article

This work contributes to both Romania’s and the European Union’s energy policies by highlighting the research results obtained within the Dunarea de Jos University of Galati, but also through the technological transfer of this knowledge to the industry. In order to improve the power quality of the nonlinear loads connected to the electrical grid, a three-phase shunt active power filter prototype based on the Harmonic Component Separation Method with a Low-Pass Filter was used. The active power filter is connected at the Point of Common Coupling to compensate for individual loads or even all of them simultaneously. Therefore, active power filters can be used to compensate for the power factor and reduce the harmonic distortion of power supplies, or for processes subsequently connected to additional nonlinear loads, thus improving the energy efficiency. The shunt active power filter prototype is composed of the power side (three-phase insulated gate bipolar transistor bridge, DC link capacitor precharge system, inductive filter) and the control side (gate drive circuits, control subsystems, signal acquisition system). The filter control strategy is based on the principle of separating harmonic components with a low-pass filter, implemented by the authors on the industrial prototype. In this paper, the main technical features of the industrial shunt active power filter prototype are specified. The authors of this paper involved three cascaded control loops: the DC link voltage control loop, the shunt active power filter current control loop and the phase-locked loop. Both simulation and experimental results for the shunt-type active power filter prototype were obtained. By analyzing the obtained waveforms of the power supply source in two cases (with and without an active power filter), a decrease in the total harmonic distortion was demonstrated, both the voltage harmonic distortion factor THDu and the current harmonic distortion factor THDi in the case of the active power filter connection. By using the Field-Programmed Gate Array processing platform, the powerful computational speed features were exploited to implement the active shunt power filter control on an experimental test bench. Conducting source current harmonics mitigation increased the efficiency of the power system by decreasing the respective harmonic Joule losses. The energy-saving feature led to the increased added value of the parallel active power filter. Through the performed laboratory tests, the authors demonstrated the feasibility of the proposed control solution for the industrial prototype. In accordance with the European Union’s Research and Technological Development Policy, the development of an innovation ecosystem was taken into consideration. The unified and efficient integration of all the specific actors (enterprises, research institutes, universities and entrepreneurs) in innovation was achieved. Full article

We present a 520 μJ microsecond burst-mode pulse fiber amplifier with a GHz-tunable intra-burst repetition rate and a nearly flat-top pulse envelope. The amplifier architecture comprises a microsecond pulse seed, a high-bandwidth electro-optic modulator (EOM), two pre-amplifier stages, a waveform-compensated acoustic-optic modulator (AOM), and two main amplifier stages. To address amplified spontaneous emission (ASE) and nonlinear effects, a multistage synchronous pumping scheme that achieved a maximum energy output of 520 μJ and has a peak power of 160 W was used. To produce a flat-topped burst pulse envelope, the AOM generates an editable waveform with a leading edge and a high trailing edge to compensate for waveform distortion, resulting in a 5 μs nearly flat-top pulse envelope at maximum energy. The laser provides an adjustable intra-burst pulse repetition rate range of 1–5 GHz through the high-bandwidth EOM modulation. The intra-burst pulse jitter time of the laser remains below 4.31 ps at different frequencies. Moreover, the beam quality of the amplifier is M²x = 1.04 and M²y = 1.1. This amplifier exhibits promising potential and can be further amplified as an optical drive source for high-power, large-bandwidth microwave photon (MWP) radar applications. Meanwhile, it is also potentially applicable as a pulse source for high-speed optical communications, the high-precision processing of special materials, and LIDAR ranging. Full article

It may be helpful to integrate multiple aircraft communication and navigation functions into a single software-defined radio (SDR) platform. To transmit these multiple signals, the SDR would first sum the baseband version of the signals. This outgoing composite signal would be passed through a digital-to-analog converter (DAC) before being up-converted and passed through a radio frequency (RF) amplifier. To prevent non-linear distortion in the RF amplifier, it is important to know the peak voltage of the composite. While this is reasonably straightforward when a single modulation is used, it is more challenging when working with composite signals. This paper describes a machine learning solution to this problem. We demonstrate that a generalized gamma distribution (GGD) is a good fit for the distribution of the instantaneous voltage of the composite waveform. A deep neural network was trained to estimate the GGD parameters based on the parameters of the modulators. This allows the SDR to accurately estimate the peak of the composite voltage and set the gain of the DAC and RF amplifier, without having to generate or directly observe the composite signal. Full article

This paper investigates Ring Beam Modulation-assisted Laser (RBML) welding as a novel approach for joining dissimilar materials, specifically aluminum and copper, which are essential in high-performance applications such as electric vehicle batteries and aerospace components. The study aims to address challenges such as thermal mismatches, brittle intermetallic compounds, and structural defects that hinder traditional welding methods. The research combines experimental and computational analyses to evaluate the impact of heat input distributions and laser modulation parameters on weld quality and strength. Three welding cases are compared: fixed center beam with variable ring beam outputs, variable center beam with fixed ring outputs, and a wobble-mode beam to enhance interfacial bonding. Computational modeling supports the optimization process by simulating heat flows and material responses, exploring various shape factors, and guiding parameter selection. Key findings include a nonlinear relationship between heat input and welding strength across the cases. Case 1 demonstrates improved weld strength with higher ring beam input, while Case 2 achieves excellent reliability with relatively lower inputs. Case 3 introduces wobble welding, yielding superior resolution and consistent weld quality. These results confirm that precise ring beam modulation enhances weld reliability, minimizes thermal distortions, and optimizes energy consumption. The manuscript advances the state of knowledge in laser welding technology by demonstrating a scalable, energy-efficient method for joining dissimilar materials. This contribution supports the fabrication of lightweight, high-reliability assemblies, paving the way for innovative applications in the automotive, medical, aerospace, and shipbuilding industries. Full article

Remote sensing images have the characteristics of high complexity, being easily distorted, and having large-scale variations. Moreover, the motion of remote sensing targets usually has nonlinear features, and existing target tracking methods based on remote sensing data cannot accurately track remote sensing targets. And obtaining high-resolution images by optimizing algorithms will save a lot of costs. Aiming at the problem of large tracking errors in remote sensing target tracking by current tracking algorithms, this paper proposes a target tracking method combined with a super-resolution hybrid network. Firstly, this method utilizes the super-resolution reconstruction network to improve the resolution of remote sensing images. Then, the hybrid neural network is used to estimate the target motion after target detection. Finally, identity matching is completed through the Hungarian algorithm. The experimental results show that the tracking accuracy of this method is 67.8%, and the recognition identification F-measure (IDF1) value is 0.636. Its performance indicators are better than those of traditional target tracking algorithms, and it can meet the requirements for accurate tracking of remote sensing targets. Full article