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The study of wave-structure interactions involving nonlinear forces would often make use of the popular hybrid frequency–time domain method. In the hybrid method, the frequency-domain analysis could firstly provide the reliable and accurate dynamic parameters and responses; then these parameters and responses are transformed to the parameters to establishing the basic time-domain equation. Additionally, with the addition of the required linear and nonlinear forces, the time-domain analysis can be used to solve for the practical problems. However, the transformation from the frequency domain to the time domain is not straightforward, and the implementation of the time-domain equation could become increasingly complicated when different modes of motion are coupled. This research presents a systematic introduction on how to implement the time-domain analysis for floating structures, including the parameter transformations from the frequency domain to the time domain, and the methods for calculating and approximating the impulse functions and the fluid-memory effects, with special attention being paid to the coupling terms among the different motion modes, and the correctness of the time-domain-equation implementation. The main purpose of this article is to provide relevant information for those who wish to build their own time-domain analyses with the open-source hydrodynamic analysis packages, although commercial packages are available for time-domain analyses.

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This study demonstrates the use of variable-speed refrigeration (VSR) technology, based on brushless DC motors, for the fast-frequency response. First, the authors derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, they consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived through transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

Terahertz (THz) plasmonic metamaterial, based on a metal-wire-woven hole array (MWW-HA), is investigated for the distinct power depletion in the transmittance spectrum of 0.1–2 THz, including the reflected waves from metal holes and woven metal wires. Woven metal wires have four orders of power depletion, which perform sharp dips in a transmittance spectrum. However, only the first-order dip at the metal–hole–reflection band dominates specular reflection with a phase retardation of approximately π. The optical path length and metal surface conductivity are modified to study MWW-HA specular reflection. This experimental modification shows that the first order of MWW-HA power depletion is sustainable and sensitively correlated with a bending angle of the woven metal wire. Specularly reflected THz waves are successfully presented in hollow-core pipe wave guidance specified from MWW-HA pipe wall reflectivity.

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations.

The intelligent monitoring of the grinding wheel wear state has the potential to enhance several key aspects of grinding operations, including wheel utilization, wheel dressing, grinding efficiency, grinding quality and so on. In this paper, it is proposed as an acoustic emission signal–based monitoring method of electroplated diamond grinding wheel wear state for C/SiC composite material groove grinding. Firstly, the full-life wear experiment of electroplated grinding wheel grinding C/SiC composites was carried out, and the connection between the acoustic emission signal and the wear state of the grinding wheel was established by frequency domain and time–frequency domain characteristics. Secondly, the time domain, frequency domain and time–frequency domain features of the signals in the stable grinding stage of C/SiC composites were extracted by wavelet packet method. Finally, based on the extracted features, the Extreme Learning Machine (ELM) was optimized by Mayfly Algorithm (MA) to realize online monitoring and intelligent recognition of grinding wheel wear. The results show that the sample classification accuracy of this method is 96.67%, which can effectively identify the different states of grinding wheel wear.

Change detection is a pivotal task in remote sensing information extraction, and leveraging the representation capabilities of large models has emerged as a promising direction in recent research. However, existing large-model-based change detection methods primarily focus on adaptation and fine-tuning strategies, while often overlooking the effective separation of true change information from background content. As a result, these methods still suffer from frequent false alarms and missed detections, especially in complex scenarios. To address these limitations, we propose a SAM fine-tuning adaptation change detection method based on Fourier frequency domain analysis difference reinforcement (SAM-FDN). In this method, we utilize the feature extraction capability of the SAM and adopt a low-rank fine-tuning strategy to construct the feature extraction backbone network of the model, extracting remote sensing image features at different time periods to enhance the model’s cognitive ability towards remote sensing images at different time periods. Furthermore, a Fourier Change Feature Extraction-Separation Module (FCEM) is designed based on Fourier frequency-domain analysis. This module separates high-frequency variation information from low-frequency invariant information, thereby enhancing differential features while suppressing invariant ones, which in turn contributes to more reliable and accurate remote sensing change detection (RSCD). Experiments conducted on three benchmark datasets demonstrate that SAM-FDN consistently outperforms existing state-of-the-art methods across various complex change detection scenarios. Ablation studies further confirm the effectiveness of the proposed coupling strategy between the SAM foundation model and the frequency-domain perception mechanism. In particular, the FCEM significantly improves the separation of meaningful change features and the suppression of irrelevant information, ultimately enhancing the model’s sensitivity to real changes and its overall detection performance.

The topic of this paper is the design of two fractional order schemes, based on a state feedback for linear integer order system. In the first one of the state feedback is associated with a fractional order integral (Iα $ I^{\\alpha }$ ) controller. In the second structure the state feedback is associated with a fractional order proportional integral (PIα $ PI^{\\alpha }$ ) controller. With such controllers, the closed loop system with state feedback described by the state equations splits in n‐subsystems with different fractional orders derivatives of the state variable. In order to find the optimal parameters value of both controllers (Iα $ I^{\\alpha }$ ) and (PIα $ PI^{\\alpha }$ ), a multi‐objective particle swarm optimization algorithm is used, with the integral of absolute error, the overshoot Mp $ M_{p}$ , the Buslowicz stability criterion are considered as objective functions. The multi‐objective integral fractional order controller and the multi‐objective proportional integral fractional order controller are applied to stabilize the inverted pendulum‐cart system (IP‐C), and their performance is compared to the fractional order controller. The simulation results of these innovative controllers are also compared with those obtained by conventional proportional–integral–derivative and fractional order proportional–integral–derivative controllers. The robustness of the proposed controllers against disturbances is investigated through simulation runs, considering the non‐linear model of the IP‐C system. The obtained results demonstrate that our approach not only leads to high effectiveness but also showcases remarkable robustness, supported by both simulation and experimental results. 1. This paper deals with two fractional order controllers using state feedback for linear integer order systems. 2. The parameters of these two controllers are calculated by using the MOPSO algorithm. 3. Experimental results show effective perfermances in both tracking and stabilizing the inverted pendulum.

The design of tuned mass dampers for reduction of seismic vibrations in multiple degree of freedom structures is also a complex problem and the optimization of design parameters of tuned mass damper are needed for the best reduction of structural responses. In the optimization process, frequency or time domain solutions can be iteratively used. In this paper, a frequency based optimization technique is presented to find design variables such as mass, period and damping ratio of tuned mass damper on the top of a structure. A music inspired metaheuristic algorithm called harmony search is employed to reach the optimum solution. The optimum results were obtained for two 10-story and one 40-story structures. According to comparisons with time domain based method, frequency domain based methods is effective to reduce maximum values and to obtain a steady stead response for critical excitations.

In order to further improve the information effectiveness of digital image transmission, an image-encryption algorithm based on 2D-Logistic-adjusted-Sine map (2D-LASM) and Discrete Wavelet Transform (DWT) is proposed. First, a dynamic key with plaintext correlation is generated using Message-Digest Algorithm 5 (MD5), and 2D-LASM chaos is generated based on the key to obtain a chaotic pseudo-random sequence. Secondly, we perform DWT on the plaintext image to map the image from the time domain to the frequency domain and decompose the low-frequency (LF) coefficient and high-frequency (HF) coefficient. Then, the chaotic sequence is used to encrypt the LF coefficient with the structure of “confusion-permutation”. We perform the permutation operation on HF coefficient, and we reconstruct the image of the processed LF coefficient and HF coefficient to obtain the frequency-domain ciphertext image. Finally, the ciphertext is dynamically diffused using the chaotic sequence to obtain the final ciphertext. Theoretical analysis and simulation experiments show that the algorithm has a large key space and can effectively resist various attacks. Compared with the spatial-domain algorithms, this algorithm has great advantages in terms of computational complexity, security performance, and encryption efficiency. At the same time, it provides better concealment of the encrypted image while ensuring the encryption efficiency compared to existing frequency-domain methods. The successful implementation on the embedded device in the optical network environment verifies the experimental feasibility of this algorithm in the new network application.

HighlightsBased on the dynamical principle and the time–frequency domain analysis method, the FAPMIM is developed.The index change of rock mass damage is analyzed from the perspective of energy and dynamics.The proposed method can identify all the noise points in the test and reduce the false alarm rate from 2.82% to 0.