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Affected by the nonlinear time-varying factors due to fault scenarios, protection relaying, and control measures, the dynamic behaviors of a power system may be significantly different from the results of previous methods. In order to analyze the oscillation characteristics of complex power systems more accurately and suppress the low frequency oscillation more effectively, this paper improves the trajectory section eigenvalue method. Firstly, the time response of a system is obtained by numerical simulation in a given fault scenario. Secondly, the algebraic variables are substituted to the differential equations along the trajectory. Thus, the original time-varying differential-algebraic equations are approximated by a set of linear ordinary differential equations, which can be updated along the trajectory. On this basis, this paper proposes a method to extract instantaneous features of the oscillation from the micro perspective. The non-equilibrium points with strong nonlinearity or critical eigenmodes are identified by the proposed method. The simulation test results of the IEEE 3-machine 9-bus system and the New England system illustrate the validity of the proposed method.

As an important component of forest ecosystem processes, leaf litterfall plays a key role in nutrient cycling and ecosystem functioning. However, monitoring litterfall dynamics in subtropical forests remains challenging due to complex community structures and asynchronous leaf phenology, which limit the applicability of remote sensing approaches developed for temperate forests. As a critical linkage between vegetation and soil carbon pools, leaf litterfall directly influences forest carbon sequestration by providing carbon inputs in the form of litter. Unlike the concentrated autumn leaf fall in temperate forests, subtropical forests exhibit complex community structures with concurrent leaf abscission and new leaf growth, limiting the applicability of temperate-focused remote sensing techniques. To address this, we collected annual leaf litterfall data from 18 plots in eastern China’s subtropical forests and integrated these with high-resolution Sentinel-2 imagery using supervised machine learning models to develop a novel monitoring method. Our results indicated that subtropical forests exhibited clear seasonal leaf litterfall peaks during spring, summer, and autumn. Sentinel-2 satellite imagery combined with supervised machine learning algorithms can effectively monitor forest leaf litterfall dynamics. Temporal models, which use multi-date monthly spectral differences (R2adj = 0.70, RMSE = 0.46, RPD = 1.86), significantly outperformed instantaneous models based on single-date canopy states (R2adj = 0.33, RMSE = 0.85, RPD = 1.24). Following variable selection, model performance improved, with R2 increasing by more than 2% in most models and the number of variables reduced by over 44%. Robustness analysis indicated that the model was spatially robust (no significant bias among sites), and despite seasonal intercept differences, the slopes were consistent, enabling reliable tracking of litterfall dynamics. Among the examined spectral indices and canopy characteristics, those reflecting canopy greenness, pigments, and structure contributed over 65%, with WV-VI, MCARI2, and LAI being most influential. Incorporating drought-sensitive water indices and soil exposure-related mineral indices further enhanced model performance. These indices may partially reflect drought stress or seasonal canopy opening. Our findings provide a new method for monitoring leaf litterfall dynamics in structurally complex subtropical forests and offer a critical theoretical basis for accurately assessing leaf fall dynamics. Our findings provide a novel and effective method for monitoring leaf litterfall dynamics in structurally complex subtropical forests, improving seasonal litterfall assessment and supporting vegetation monitoring, with potential implications for ecosystem- and carbon-related studies.

Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, traditional Variational Mode Decomposition (VMD) and Hilbert Transform (HT) suffer from suboptimal decomposition levels (K) and spectral asymmetry. This paper proposes an improved VMD-HT framework to enhance feature extraction from short-term Inertial Measurement Unit (IMU) signals. First, an instantaneous-frequency-driven adaptive VMD method is developed to mitigate mode mixing by automatically determining the optimal K. Second, an information-enhanced instantaneous energy density (IEIE) feature is introduced. By fusing kinetic energy from both positive and negative frequency domains, this feature restores the spectral symmetry of the energy representation, precisely quantifying fine motion variations and compensating for information loss caused by the limited temporal span. Experimental results on PAMAP2, WARD, and a self-collected dataset, NOITOM, demonstrate the method’s effectiveness. With a 0.5 s window, the proposed model achieves outstanding recognition accuracies of 93.60%, 96.41%, and 97.22%, respectively, outperforming state-of-the-art approaches in capturing transient short-term information.

The paper proposes an automatic modulation recognition scheme based on instantaneous features of intercepted signals. The modulation classifier can discriminate modulations such as Amplitude Modulation (AM), Double Side Band (DSB), Single Side Band (SSB), Frequency Modulation (FM), M-ary Amplitude Shift Keying (M-ASK), M-ary Frequency Shift Keying (M-FSK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) without any prior information. The scheme is with simple structure, computationally simpler, and suitable for real-time processing. And the recognition parameters are anti-noise, and insensitive to frequency offset, phase offset and timing error. To evaluate the performance of the scheme, several experiments with signals in Additive White Gaussian Noise (AWGN) channel were carried out in the MATLAB by varying the values of both the main modulation parameters and the signal-to-noise ratios (SNRs). The results show that the approach can achieve high recognition accuracy even at low SNR.

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

To investigate the effect of rock microstructure on its macroscopic properties under instantaneous high temperature, we employed nuclear magnetic resonance and triaxial apparatus to examine the microstructure and macroscopic characteristics of samples. Additionally, Shannon entropy was introduced as a measure for characterizing microscopic pore and fragment distribution characteristics. We explored the relationship between pore proportion, density, pore size distribution entropy (PSDE), and fragment distribution entropy (FDE) with temperature variations. Furthermore, we utilized entropy weight to assess the thermal sensitivity of pores and calculate the damage coefficient. Finally, we extensively investigated the intrinsic relationship between PSDE and peak intensity, permeability, as well as dissipated energy. The results demonstrate that the microstructure of red sandstone undergoes a significant change after 400 ℃. Beyond this temperature, T 2 exhibits substantial variations, with the proportion of micropores transitioning from an increasing to decreasing trend and the proportion of mesopores shifting from a decreasing to increasing pattern. Except at 400 ℃, there is an overall increase in pore density. Additionally, after 400 ℃, the distribution entropy decreases for micropores, macropores, and total pore diameter, while it increases for mesopores. The peak intensity and permeability of PSDE follow an exponential function, whereas its dissipative energy conforms to a linear relationship. Before 400 ℃, micropores exhibit the highest thermal sensitivity; however, macropore thermal sensitivity becomes dominant after this point, while both macropore and mesopore sensitivities increase significantly as micropore sensitivity declines notably. Employing entropy weight calculations effectively enhances accuracy when determining damage degree based on T 2 peak area measurements. FDE and fracture area show an upward trend with increasing temperature until reaching their maximum value at 200 ℃ before rapidly declining after that; conversely, fractal dimension of fragments displays an opposite behavior. This study aims to provide fundamental insights into the stability assessment of surrounding rock in underground coal gasification and geothermal mining. Highlights A rock digital twin framework incorporating a data assimilation algorithm was developed. The constitutive model was iteratively calibrated using observational data. Rock parameters were probabilistically estimated, and their uncertainties were assessed. The stress-strain behavior of rocks under triaxial tests was predicted.

High-frequency current detection of partial discharge (PD) at transformers on-site faces complex noise interference, which severely impacts the accuracy of PD detection. To address this issue, an intelligent interference suppression algorithm for PD signals based on adaptive waveform feature recognition is proposed. First, a 10 MHz high-pass filter is applied to eliminate the influence of periodic narrowband interference on the zero-crossing count of the time-series. Non-pulse noise is removed based on the instantaneous zero-crossing density of the signal. Next, the start and end times of each pulse are determined, and the corresponding waveform segments are extracted from the original signal to form a pulse array. Subsequently, waveform features of the pulses are extracted, and discrimination thresholds for the feature parameters are calculated based on univariate analysis. Finally, each pulse is adaptively identified based on its waveform features, and PD signals are screened out. The proposed algorithm was tested using PD signals superimposed with on-site noise as well as field-measured signals. The results demonstrate that the algorithm can intelligently identify PD signals and significantly reduce PD signal attenuation, exhibiting excellent suppression effects on complex noise interference in on-site PD detection at transformers.

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier.

This research investigated the feasibility of applying hardware order-tracking (HOT) and segmentalized amplitude normalization (SAN) to enhance the diagnosis of multiple bearing defects at different levels under varying rotation speeds. The vibration of operating bearings may present an energy variation phenomenon due to different levels of bearing defects, while the fluctuation of vibration amplitude may be attributable to changes in rotation speeds. These two factors inevitably interfere with each other when diagnosing bearing defects at multiple levels and classes under varying rotation speeds. In this paper, the research focuses on conducting an in-depth analysis of signal signatures, followed by providing a physical insight into feature extraction. Consequently, it enables the application of simple machine learning methods to accurately diagnose various bearing defects, even when dealing with significantly different patterns in training and testing data due to varying rotation speeds. To verify the effectiveness of the proposed SAN method for cases involving varying rotation speeds, the training and testing sets used datasets (vibration measurements) corresponding to different rotation speed profiles. The experimental and analytical results revealed that the proposed SAN method can normalize datasets with disparate vibration patterns, and alleviate the coupling of vibration energy variation and shaft rotation speed. This enhancement resulted in approximately 18.6% increase in the accuracy of bearing diagnosis for cases involving varying rotation speeds.

Drive belt degradation affects the positional accuracy and repeatability of wafer-handling robots, leading to positional errors. This study developed a method for noninvasive diagnosis of drive belt degradation in wafer-handling robots using features extracted from motor current data. The motor current generating the torque is monitored for diagnosis, eliminating the need for additional sensors. Features are extracted by monitoring instantaneous changes in the motor current based on the operating characteristics of the wafer-handling robot. The diagnosis relies on a feature called the instantaneous current variation rate and a modified sequential probability ratio test (SPRT). An experiment with a five-axis wafer-handling robot confirmed that the distribution of the instantaneous current variation rate varies depending on the level of drive belt degradation and the modified SPRT successfully diagnoses drive belt degradation without generating false alarms.