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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.

The increasing aggregation of renewable-based distributed generating units besides the impressive growing usage of non-linear loads raises unwanted challenges for traditional power terms definition in power engineering. This fact consequently affected the performance of the conventional control frameworks and industrial compensation techniques. In this study, the authors aim to provide an insightful summary over the most recognised time domain-based instantaneous power theories and discuss their advantages and disadvantages within a comprehensive mathematical-conceptual and applicational framework for professionals who are using instantaneous power theories within the smart grid applications. They conclude that there is still a need for a modified power theory which can be validated under non-sinusoidal-unbalanced load/source conditions respecting the physical meaning of different power and current components.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

Summary Incipient faults in underground shielded cables can affect greatly distribution system reliability. These faults are usually a consequence of a gradual aging process on cable isolation material and over time become permanent faults. This paper presents an incipient fault location method for distribution networks with underground shielded cables. The fault location method is composed by a time domain system model and a parameter estimation approach. The model is derived by using phase component representation of voltage and current signals, considering 1‐terminal measurements and realistic hypotheses. Inherent characteristics of underground distribution networks with shielded cables and incipient faults, as unbalanced operation, complete line model, and arcing phenomena are considered during the problem statement. The time‐domain model is an overdetermined system of linear equations. Incipient fault location is estimated through a parameter estimation approach, based on a non‐negative weighted least square estimator. Input data are preprocessed with smooth and curve‐fitting approaches. Output data are postprocessing using a back substitution approach. Validation using real‐life distribution network with underground shielded cables data is presented. Comparative test results with the state of art characterize the proposed approach precision and robustness. High accuracy, easy to implement methodology without hard‐to‐design parameters, and only local terminal measurements consideration highlight potential aspects for real‐life applications.

The reactor pressure vessel (RPV) in onshore nuclear power plants is typically analysed for fatigue life by considering the temperature, internal pressure, and seismic effects using a simplified time-domain fatigue analysis. In contrast, the frequency-domain fatigue analysis method is commonly employed to assess the fatigue life of ship structures. The RPV of a floating nuclear power plant (FNPP) is subjected to a combination of temperature, internal pressure, and wave loads in the marine environment. Consequently, it is essential to effectively integrate the frequency-domain fatigue analysis method used for hull structures with the time-domain fatigue analysis method for RPVs in FNPPs or, alternatively, to develop a suitable method that effectively accounts for the temperature, internal pressure, and wave loads. In this study, a quasi-time-domain method is proposed for the fatigue analysis of RPVs in FNPPs. In this method, secondary components of marine environmental loads are filtered out using principal component analysis. Subsequently, the stress spectrum induced by waves is transformed into a stress time history. Fatigue stress under the combined influence of temperature, internal pressure, and wave loads is then obtained through a stress component superposition method. Finally, the accuracy of the quasi-time-domain method was validated through three numerical examples. The results indicate that the calculated values obtained by the quasi-time-domain method are slightly higher than those obtained by the traditional time-domain method, with a maximum deviation of no more than 24%. Additionally, the computation time of the quasi-time-domain method is reduced by 98.67% compared to the traditional time-domain method.

Background Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. Methods Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. Results Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. Conclusions Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.

The finite‐difference time‐domain (FDTD) method based on the iterated Crank–Nicolson (ICN) scheme is extended to a frequency‐dependent version. The Drude model is used to express a metal dispersion, which is incorporated into the iterated Crank–Nicolson formulation with the trapezoidal recursive convolution technique. The validity of the present finite‐difference time‐domain method with convolutional perfectly matched layers is discussed through the analysis of a metal‐insulator‐metal plasmonic waveguide. Numerical results obtained from a two‐iteration technique are found to agree well with those from the traditional explicit finite‐difference time‐domain method. The transmission spectra are calculated using the proposed FDTD method based on the iterated Crank‐ Nicolson scheme. The result obtained from the proposed method is in perfect agreement with that obtained from the traditional explicit FDTD method.

Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method. This study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted.

In this paper, an innovative error‐based virtual composite‐axis disturbance rejection backstepping control strategy is proposed for electro‐optical tracking systems. Tracking accuracy cannot be improved by conventional composite axis structures where target position, velocity and acceleration are unknown and immeasurable. Our proposed method, however, operates without the need for target trajectory input signals or additional sensors. It solely relies on error information to adeptly simulate the compound axis system's functionality. Notably, its error suppression characteristics amalgamate dual‐axis suppression features, substantially augmenting tracking performance. Moreover, to further optimize trajectory tracking and counteract the disturbances and uncertainties within the virtual composite axis, a backstepping control strategy is integrated with disturbance rejection. Remarkably, this approach achieves a 31.89% leap in tracking accuracy and a 73.87% boost in disturbance rejection performance. The effectiveness and superiority of the method have been thoroughly corroborated via simulations and experiments. In this paper, an error‐based virtual composite‐axis disturbance rejection backstepping control strategy is proposed for the electro‐optical tracking system that cannot improve the tracking accuracy by the composite‐axis structure because the target position, velocity, and acceleration are unknown and cannot be measured directly. This method operates without input signals r and additional sensors, relying solely on error information to effectively simulate the functionality of compound axis systems. To further improve the trajectory tracking performance and address the disturbances and uncertainties present in the virtual composite axis, the method combines a backstepping control strategy with disturbance rejection.

Background The widely used rolling element bearings in rotating machines undergo progressive degradation with continuous operation. To identify bearing faults, complex time-frequency based signal processing techniques and high-end deep neural network algorithms have been used to perform fault classification, which is time-consuming. Method In this paper, the focus was given to replace the complex time-frequency domain signal processing techniques by incorporating a simple time-domain based methodology. Initially, the vibration signature of different bearing faults was acquired at three different speeds and was directly converted into images by 2D-Vibration Imaging (2D-VI) technique using an overlapping-based moving window. The extracted images were fed into Convolutional Neural Network (CNN) for automatic feature extraction, followed by classification using Support Vector Machine (SVM). Results and validation Separately, time-frequency spectrums were also extracted to compare the effectiveness of the proposed methodology. Furthermore, the proposed methodology was validated on the bearing dataset of combined faults and Case Western Reserve University (CWRU). Conclusion The experimental results showed that the proposed methodology has the potential to replace the conventional approach by consuming less computational time without affecting classification accuracy.