Adaptive Mode Research Articles

Power quality disturbance signal classification in microgrid based on kernel extreme learning machine

AbstractThis paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA–KELM achieves high classification accuracy and rapid convergence for complex PQD signals.

Adaptive cognitive tasks for mental fatigue: An innovative paradigm for cognitive loading in human performance

ObjectivesMental fatigue (MF) can impair cognitive and physical performance in sport. We tested the hypothesis that a shorter adaptive Time Load Dual Back (TLDB) task induces MF faster than a longer Stroop; and subsequently impairs cognitive and intermittent running performance. DesignThis study employed a randomized within-participant design. Methods25 trained individuals performed a Yo-Yo test after one of four experimental conditions (30-min Stroop, 20-min and 10-min TLDB tasks, and active control). Cognitive performance was assessed using the Psychomotor Vigilance Task (PVT) before and after the experimental conditions. Measures of mood, workload, MF, RPE, heart rate (HR), heart rate variability (HRV) and blood lactate were collected. ANOVAs determined the effect of the 4 conditions. ResultsStroop and 20-min TLDB conditions impaired running performance similarly (p = .015), while no differences are reported for 10-min TLDB and Control. No significant differences in physiological parameters were reported during the Yo-Yo test although RPE was significantly higher in the Stroop and 20-min TLDB conditions (p = .014). Stroop and both TLDBs conditions impaired PVT's cognitive performance (p = .029), MF (p = .012), mental demand (p < .001), HR (p = .021) and HRV (p = .033); with 20-min TLDB task having the higher significant impact. Mood alterations were similar between Stroop and TLDB conditions. ConclusionsIntermittent running (Yo-Yo) and cognitive (PVT) performances, and subjective ratings were impaired by 30-min Stroop and 20-min TLDB tasks; while 10-min TLDB did not to impair performance. Shorter adaptive modes seem to be more effective in inducing MF and could have relevant clinical applications to assess conditions such as traumatic brain injury and concussion.

Fault diagnosis of railway freight car rolling bearings based on ACMD and improved MCKD

To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions, this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition (ACMD) and an optimized Maximum Correlation Kurtosis Deconvolution (MCKD) using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation (SCSSA). Firstly, ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions (IMFs). Then, the IMFs are filtered according to the Gini coefficient indicator, with the IMF having the largest Gini coefficient selected as the optimal component. Secondly, the SCSSA is employed to iteratively optimize the filter length L, fault signal period T, and displacement parameter M in the MCKD algorithm, determining the optimal parameter combination for MCKD. This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis. The optimized MCKD is then applied to the optimal component, and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum. To verify the effectiveness and generalizability of the proposed method, simulations, experimental signals from the Case Western Reserve University Bearing Center, and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults. The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Examining Sustainable Urban Design Pattern by Explaining the Compatibility Model based on Density in Residential Fabrics of District 4th in Tehran City

Concerns about energy and climate change have taken center stage in urban studies in recent years. Buildings are the primary focus of the problem of maximizing energy efficiency in cities, and among the energy Additionally, the most crucial aspects of urban energy efficiency research are consumption, solar radiation energy on the one hand, and thermal energy demand on the other. These factors, together with other climatic factors, primarily influence the standard of life in urban areas. The main aim of research is to establish a model of sustainability in the physical form of the city, the primary objective of the current research is to extract the ideal framework and also the proposed model in the form of a structure for the concept of urban design in urban morphology, which is, on the one hand, the architectural scale of the buildings of the urban fabrics. In terms of structure, the current research is analytical-descriptive, and the method of data collection is in the form of documentation, survey, modeling and simulation using energy analysis software in the field of energy with a micro-analytical approach of urban climate such as Envi-met. For this reason, 4 morpho-types were evaluated in the context of the 4th district of Tehran, and the findings of the research show the difference between the optimal state and the current state of the evaluated parameters, and the amount of difference and the effect of added density in each morphological type of the effective range based on the index It explains that the high-rise building type has the most positive changes in the proposed conditions, and the compact types have lower values respectively. In the future researches, other morphological variables and adaptation modes with climatic parameters can also be investigated in order to achieve energy efficiency in other modes. DOI: https://doi.org/10.52783/pst.545

Understanding the phenomenological experiences of schema therapy for those with an eating disorder.

Schema therapy expands traditional cognitive-behavioral models, weakening early maladaptive schemas and schema modes while strengthening adaptive modes. This study investigated participant experiences of schema therapy for eating disorders, focusing on schema modes and the eating disorder voice, how these maintained disordered eating, and how therapy helped. Semistructured online video interviews with clients receiving schema therapy for eating disorders (N=10) were analyzed using interpretative phenomenological analysis. Four group experiential themes were developed: (1) adverse experiences, typically in childhood and adolescence, (2) interpersonal relationships, especially with primary caregivers and the benefits of a good therapeutic relationship, (3) self-awareness of schema modes and the eating disorder voice and their impact on participants' eating disorders, and (4) recovery using schema concepts, including finding one's inner child, better self-management, and ambivalence about recovering. Overall, schema therapy was perceived as beneficial, specifically regarding participants' awareness of their inner child, development of their eating disorder, and awareness of their eating disorder voice. Participants expressed a growing positive sense of agency, connecting with their inner child's needs and developing a connection to their healthy adult mode. They also felt that schema therapy had equipped them with the tools to strengthen their healthy adult mode, while simultaneously weakening their maladaptive modes.

Homeocurvature adaptation of phospholipids to pressure in deep-sea invertebrates.

Hydrostatic pressure increases with depth in the ocean, but little is known about the molecular bases of biological pressure tolerance. We describe a mode of pressure adaptation in comb jellies (ctenophores) that also constrains these animals' depth range. Structural analysis of deep-sea ctenophore lipids shows that they form a nonbilayer phase at pressures under which the phase is not typically stable. Lipidomics and all-atom simulations identified phospholipids with strong negative spontaneous curvature, including plasmalogens, as a hallmark of deep-adapted membranes that causes this phase behavior. Synthesis of plasmalogens enhanced pressure tolerance in Escherichia coli, whereas low-curvature lipids had the opposite effect. Imaging of ctenophore tissues indicated that the disintegration of deep-sea animals when decompressed could be driven by a phase transition in their phospholipid membranes.

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.

Solar photovoltaic power forecasting system with online manner based on adaptive mode decomposition and multi-objective optimization

Constructing an accurate and reliable solar photovoltaic (PV) power forecasting system is crucial for smart grid management and dispatch. However, due to the intermittent, non-stationary and random nature of solar energy, current methods cannot effectively capture the dynamic change patterns of PV data, resulting in a forecasting accuracy that cannot satisfy the requirement for stable operation of smart grids. To address this issue, in this paper, a new solar PV power forecasting system is proposed based on a new adaptive mode decomposition method that combines machine learning models. First an adaptive mode decomposition method is proposed for adaptively eliminating high-frequency information of PV data, mitigating the impact of high noise on the forecasting performance. Then the powerful mapping ability of machine learning model is utilized to construct a solar PV power generation combination forecasting system. The system adopts a multi-objective optimization strategy to determine the optimal weights of the combination model, which reduces the volatility of the forecasting results and enhances the stability of the forecasting system. Experimental results demonstrate that the proposed system achieves the best overall performance with online manner on 10 state-of-the-art baselines. Furthermore, the evaluation analysis by Diebold–Mariano test, improvement ratio and sensitivity analysis further show that the stability and accuracy of the proposed system outperforms the comparative baselines.

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (Kx, Ky) ranging between 0.01–0.1 radm−1 and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R2) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms−1. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.

Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters

Achieving multivariate long-distance forecasting of shield machine tunneling parameters remains a challenge due to the huge number of tunneling parameters and the complexity of the variation pattern. To solve this problem, a long-distance forecasting method called Adaptive Variational Mode Decomposition and Multi-Stage Stabilized Transformer-based (AVMD-MST) for multiple tunneling parameters is proposed. It uses adaptive VMD and normalization to stabilize the pre-processed tunneling parameters, and the stabilized transformer is designed to establish relationships between historical and future data. The results on two different projects show that the MAPE of the proposed method decreases on average by 12.31%–36.8% for the 180th step predictions compared to the state-of-the-art algorithms. Therefore, the idea of stabilization-prediction-inverse stabilization can achieve high precision multi-variable long-distance forecasting. In the future, geological information overcasting will be carried out on the basis of the multivariate long-distance forecasting model, which can help the driver to determine the tunneling strategy.

Adaptations of masculinity: mapping the affective power of Achilles and Patroclus

Abstract The Greek myth of Achilles and Patroclus, two of the heroes of the Trojan War, has served various ideological purposes in adaptive revisions. The central question of this article is how the story lends itself to different interpretations and ideological projects across time. Building on concepts of affect, power, and cartographical reading, as described by Gilles Deleuze, the analyses highlight the potential of the myth to do different things. The examples analysed are Christa Wolf’s novel Kassandra, Madeline Miller’s novel The Song of Achilles, and a figure skating routine by French elite skater Kevin Aymoz. A comparison of these adaptations shows how the myth can be used to celebrate alternative notions of masculinity and challenge homophobic discourses. Furthermore, the comparison highlights figure skating as a mode of adaptation where a performer becomes a character, thus underlining the affirmative potential of the sport.

16.2-μW Super Class-AB OTA with current-reuse technique achieving 130.3-μA/μA FoM

In this paper, a single-stage Super Class-AB operational transconductance amplifier (OTA) using current reuse technique is presented. It is based on two scaled current mirrors located at the tail current nodes of input pairs, reusing the current through the cross-coupled input pairs by a current gain factor of α, and delivering new currents to the output branch, enhancing the overall transconductance (Gm) by a factor of (1+α). With adaptive biasing and local common mode feedback (LCMFB), slew rate (SR), open loop gain (AOL) and gain-bandwidth product (GBW) are improved. An prototype has been designed in TSMC 0.18-μm CMOS process. Post-simulation shows the positive and negative slew rate of 22V/μs and −38.3V/μs, respectively, a GBW of 3.72 MHz, a DC gain of 65.6 dB with a phase margin of 69.2° for a capacitive load of 70 pF. The proposed OTA consumes a total power of 16.2 μW under a 1.8V supply voltage.

Implicated Realism and the Environmentalism of the Rich in Ben Lerner’s 10:04

Abstract: This essay argues that Ben Lerner’s novel 10:04 (2014) employs “implicated realism” to represent the environmentalism of the rich. Implicated realism is a self-reflexive aesthetic that reveals how the foundations of literary realism—narrative description, bourgeois settings, an emphasis on daily life—rely upon the forms of exploitation that have also produced the climate crisis. I demonstrate that implicated realism in 10:04 , paradoxically, consists of both hyperrealism and realist failure. Lerner’s novel applies hyperrealist description to seemingly innocuous scenes, uncovering their implication in the uneven distribution of environmental harm. Such hyperrealism exists alongside realist failure, which 10:04 both thematizes and performs. Through realist failure, then, implicated realism confronts the compromised history of realism and its association with possessive individualism and extractive capitalism. Consequently, although many ecocritics discount realism’s ability to represent climate change, this essay identifies implicated realism as a self-reflexive and adaptive mode.

EIF4E integrates into stress response

In this issue, Diamond etal.1 and Kim etal.2 report that depletion of eIF4E leads to translational upregulation of GCN4, a key player in the integrated stress response, in an eIF2α phosphorylation-independent manner, suggesting a new mode of translational adaptation.

Multivariate iterative nonlinear chirp mode decomposition: principles and applications

Abstract The rotor-bearing system (RBS) of a hydroelectric unit includes multiple components such as the rotor, rotor shaft, coupling, turbine, and main shaft. There is a strong coupling relationship between the vibration signals of multiple bearing sections. At the same time, the vibration signal of the RBS contains rich frequency components, which can effectively reflect the operating state of the shaft system of the unit. Therefore, synchronous signal analysis across multiple rotor bearing sections is required to completely grasp the operating status of the unit. In this study, the Multivariate Iterative Nonlinear Chirp Mode Decomposition (MINCMD) is proposed to analyze signals on multiple sections simultaneously. MINCMD follows the step-by-step extraction idea of Adaptive Multivariate Chirp Mode Decomposition (AMCMD), but it adopts a new algorithm framework by modifying the objective function. Furthermore, AMCMD does not converge well without a good initial instantaneous frequency (IF). Therefore, a method based on parameterized demodulation (PD) is introduced in MINCMD to solve the multi-channel frequency initialization problem. The effectiveness of the proposed algorithm is verified through a simulation test and real data from hydroelectric units.

Research on New Energy Transmission Line Fault Localization Method Based on APSVMD-NTEO

Abstract Given the new energy transmission line fault, traveling wave head detection difficulties, wave speed uncertainty, positioning errors, and other problems, this paper proposes a new Teager energy operator based on adaptive parameter selection variational mode decomposition (APSVMD) and new energy transmission line fault location method (NTEO). The envelope fuzzy entropy is introduced to select the key parameters of SVM, and the sampling range of the Teager energy operator (TEO) is improved to effectively improve the capture accuracy of the wave head. In terms of fault location, by improving the double-ended traveling wave method, the uncertainty of wave speed and the effect of actual line length error is eliminated, and the ranging error is reduced.

An Adaptive Indicator Optimization Ensemble Empirical Mode Decomposition Method and Its Application on the Denoising of BeiDou B1I Signal

An Adaptive Indicator Optimization Ensemble Empirical Mode Decomposition Method and Its Application on the Denoising of BeiDou B1I Signal