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Measurement of the cervical range of motion (CROM) is significant for early diagnosis of cervical spondylosis and determination of the severity of the disease. Aiming at the problem of convenient and continuous measurement of CROM and the reduction of the impact of data fluctuations, this paper proposes a low‐cost method for testing and analyzing CROM. This paper analyzes the correspondence between smartphone orientation sensor angle and CROM, obtains smartphone orientation sensing data, and uses the extreme‐point symmetric mode decomposition method by calculating the energy of the difference value to extract the adaptive global mean curve of the CROM data, and calculates the cervical ROM. The statistical analysis of the CROM test results is carried out. Results show that the method proposed can obtain the CROM including flexion and extension, lateral flexion, and rotation in a single measurement, which has the advantages of continuous measurement and low cost. [ABSTRACT FROM AUTHOR]

This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio‐inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110‐kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on‐site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration‐based detection framework presents an innovative application of nature‐inspired computing for power infrastructure health monitoring. [ABSTRACT FROM AUTHOR]

This paper investigates the multi‐dimensional non‐linear exponential smoothing prediction method under different weight coefficients, so as to realize the short‐term prediction of the original wind power output. Based on the current research on wind power fluctuation prediction, this paper firstly proposes a new multi‐dimensional nonlinear exponential smoothing prediction method. Based on different weight coefficients, the corresponding short‐term forecast volatility is obtained. Then, the wavelet packet decomposition method is used to further realize the secondary smoothing of the prediction wind power fluctuations. Hybrid energy storage system (HESS) plays an important role in wind power fluctuation suppression from different frequency bands. However, the traditional fixed frequency power allocation method significantly degrades the performance of the accuracy of the algorithm. Therefore, this paper proposes a frequency conversion entropy strategy, which combines energy value with wavelet packet decomposition. By calculating the energy components of different frequency bands, the fluctuation can be divided effectively. Finally, simulation and experiment discussions show that the proposed algorithm in this paper can realize the power allocation effectively compared to the traditional one. And it further verifies the feasibility and reliability of the strategy proposed in this work. [ABSTRACT FROM AUTHOR]

The optimization of Multidimensional Knapsacks with Family‐Split Penalties has been introduced in the literature as a variant of the more classical Multidimensional Knapsack and Multi‐Knapsack problems. This problem deals with a set of items partitioned in families, and when a single item is picked to maximize the utility, then all items in its family must be picked. Items from the same family can be assigned to different knapsacks, and in this situation split penalties are paid. This problem arises in real applications in various fields. This paper proposes a new exact and fast algorithm based on a specific Combinatorial Benders Cuts scheme. An extensive experimental campaign computationally shows the validity of the proposed method and its superior performance compared to both commercial solvers and state‐of‐the‐art approaches. The paper also addresses algorithmic flexibility and scalability issues, investigates challenging cases, and analyzes the impact of problem parameters on the algorithm behavior. Moreover, it shows the applicability of the proposed approach to a wider class of realistic problems, including fixed costs related to each knapsack utilization. Finally, further possible research directions are considered. [ABSTRACT FROM AUTHOR]

High energy consumption around the world and concerns over climate change and high greenhouse gas emissions have led to a significant increase in using renewable energy resources, particularly wind power plants. In spite of using small wind farms in distribution networks as distributed generation, the interest in employing large‐scale wind farms in transmission levels has grown significantly in recent years. The presence of large‐scale wind farms with a synchronous generator to supply the energy demand can affect the power swing parameters. These effects are in a way that can mislead power swing detection methods and cause undesirable tripping of transmission line due to mal‐operation of distance relay against power swing. Therefore, in this paper, a method has been presented based on modified empirical mode decomposition (EMD) method to differentiate fault from power swing in the networks connected to the large‐scale wind farms. The results prove the efficiency of the presented method in detecting the power swing in large‐scale wind farm networks. Finally, a comparison is conducted between the proposed method and similar methods which proves the efficiency and superiority of the proposed method in terms of noise tolerance, detection fault detection speed, and multi‐mode power swing detection compared to the current methods. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment. [ABSTRACT FROM AUTHOR]

The paper presents a new method for the decomposition of the horizontal wind divergence among linear waves on the sphere: inertia‐gravity (IG), mixed Rossby‐gravity (MRG), Kelvin and Rossby waves. The work is motivated by the need to quantify the vertical velocity and momentum fluxes in the tropics where the distinction between the Rossby and gravity regime, present in the extratropics, becomes obliterated. The method leads to divergence power spectra as a function of latitude and pressure. The spectra follow the same power laws as the vertical kinetic energy spectra for different wave types. The key novel aspect of the work is the coexistence of wave types at the same zonal wavenumbers. Applied to ERA5 data in August 2018, the new method reveals that the zonally‐integrated Kelvin wave divergence makes up about 19% of the upper‐troposphere divergence within 10°S–10°N. The MRG wave divergence has four times smaller magnitude. The relative roles of the two waves vary with scale. Overall small roles of the Kelvin and MRG waves in the tropical divergence is explained by decomposing their kinetic energies into rotational and divergent parts. The beta effects produces less then 5% of the tropospheric divergence associated with Rossby waves. The majority of divergence belongs to IG modes and is nearly equipartitioned between the eastward‐ and westward‐propagating IG modes in the upper troposphere, whereas stratospheric partitioning depends on the background flow. Plain Language Summary: The horizontal circulation is commonly discussed using divergence and vorticity instead of the two wind components. The advantage is that vorticity and divergence characterize the Rossby wave and inertia‐gravity (IG) wave dynamics, respectively, thereby offering an easier understanding of wave processes. The two wave regimes are well separated in extratropics also in the wavenumber and frequency space. However, there is no regime separation in the tropics where IG waves shape variability at all scales including low‐frequency variability. Furthermore, two additional waves are present, Kelvin waves and mixed Rossby‐gravity (MRG) waves that span the wavenumber and frequency space between Rossby and IG waves. This study aims at a better understanding of partitioning between divergent and rotational components of equatorial waves. In particular, Kelvin and MRG waves have significant variances at the same scales as Rossby and IG waves calling for an approach that quantifies their divergence at the same wavenumbers. This paper develops such an approach for the spherical atmosphere and demonstrates it on the ERA5 data in August 2018. One important result is that the Kelvin and MRG wave constitute up to approximately 25% of divergence in the tropical upper troposphere, mainly in Kelvin waves. Key Points: A new method couples divergence with wave circulation as a function of latitude and pressure levelDivergence quantification accounts for wave types sharing the same zonal scale, which is crucial for disentangling tropical flowsApplied to ERA5, the new method highlights vortical nature of mixed Rossby‐gravity waves and large‐scale Kelvin waves [ABSTRACT FROM AUTHOR]

The stabilization problem for uncertain discrete‐time singular Markovian jump systems (DSMJSs) with time‐varying delays is comprehensively covered in this paper. An updated Lyapunov–Krasovskii functional is presented via a discrete state decomposition method. With the help of this constructed Lyapunov–Krasovskii functional, some delay‐ and mode‐dependent sufficient conditions for the open‐loop DSMJSs are derived. Based on these circumstances, a memory mode‐dependent state feedback control is used to create a closed‐loop DSMJS with parameter uncertainties that is regular and causal. And then, the stochastically admissible conditions are attained. Through the exact calculation of each decomposition component for the designed memory state feedback controller, the intended memory state feedback controller settings are determined. It should be mentioned that the algorithm suggested in this article expands the controller design's feasibility and flexibility. The numerical results show how the approach is superior to previous ones, and the given results are less conservative. [ABSTRACT FROM AUTHOR]

Summary: Uncoded space‐time labelling diversity (USTLD) is a recent scheme that improved the error performance compared to conventional multiple‐input, multiple‐output systems. Thus far, USTLD has suffered from limited achievable data rates, as the original model uses only two transmit antennas. This motivates for the work in this paper, where the USTLD model is extended to allow for any desired number of transmit antennas. An analytical bound for the average bit error probability of this high‐rate USTLD (HR‐USTLD) system is derived. This expression is verified using the results of Monte Carlo simulations, which show a tight fit in the high signal‐to‐noise ratio region. The increased data rates associated with larger transmit antenna arrays in HR‐USTLD systems come at the cost of increased detection complexity. Therefore, this paper studies the application of low‐complexity detection algorithms based on the popular QR decomposition technique and proposes a new algorithm specifically designed for HR‐USTLD systems. Analysis of this algorithm in terms of accuracy and computational complexity is also provided and benchmarked against maximum‐likelihood detection (MLD). It is shown that the proposed algorithm achieves near‐MLD accuracy, while reducing complexity by 79.75% and 92.53% for the respective 4 × 4 16QAM and 4 × 5 16PSK HR‐USTLD systems investigated. [ABSTRACT FROM AUTHOR]

Multi‐resolution analysis methods can reveal the underlying physical dynamics of nonstationary signals, such as those from lightning. In this paper we demonstrate the application of two multi‐resolution analysis methods: Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) in a comparative way in the analysis of electric field change waveforms from lightning. EEMD and VMD decompose signals into a set of Intrinsic Mode Functions (IMFs). The IMFs can be combined using distance and divergence metrics to obtain noise reduction or to obtain new waveforms that isolate the physical processes of interest while removing irrelevant components of the original signal. We apply the EEMD and VMD methods to the observations of three close Narrow Bipolar Events (NBEs) that were reported by Rison et al. (2016, https://doi.org/10.1038/ncomms10721). The ΔE observations reveal the occurrence of complex oscillatory processes after the main NBE sferic. We show that both EEMD and VMD are able to isolate the oscillations from the main NBE, with VMD being more effective of the two methods since it requires the least user supervision. The oscillations are found to begin at the end of the NBEs' downward fast positive breakdown, and appear to be produced by a half‐wavelength standing wave within a weakly‐conducting resonant ionization cavity left behind in the wake of the streamer‐based NBE event. Additional analysis shows that one of the NBEs was likely initiated by an energetic cosmic ray shower, and also corrects a misinterpretation in the literature that fast breakdown is an artifact of NBE‐like events in interferometer observations. Plain Language Summary: This paper investigates the application of mode decomposition techniques to the analysis of Narrow Bipolar Events (NBEs). NBEs are high‐power discharges that often occur as the initiating event of lightning flashes, and are produced by streamer‐based activity called fast positive breakdown. We apply Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) methods to three NBEs that were observed at Langmuir Laboratory in New Mexico to extract and interpret oscillatory behavior that occurred following the NBEs. We show that both EEMD and VMD separate the oscillations from the strong electric field change of the parent NBE, with VMD being the preferred choice. The resulting waveforms are indicative of a shock‐excited residual process that lasts for tens of microseconds, beginning at the end of the downward fast breakdown activity. Although questions remain about the physical mechanism of the oscillations, they appear to be caused by the NBE's streamers creating a weakly‐conducting resonant cavity in its wake that supports half‐wavelength standing wave oscillations, analogous to the vibrations of a plucked guitar string. Key Points: Ensemble Empirical Mode Decomposition and Variational Mode Decomposition separate out Narrow Bipolar Event (NBE) oscillations for further studies of physical mechanisms responsible for oscillationsThe results show the oscillations initiate not during the NBE but at the end of the fast positive breakdown processThe oscillatory behavior indicates that residual ionic conductivity creates a resonant cavity which emits half‐wavelength radiation [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed‐time robust control problem of a class of nonlinear time‐delay systems, and proposes some new prescribed‐time robust stabilization results. First, by applying the full decomposition method based on the delay term, the paper obtains an equivalent model of original system and promotes existing prescribed‐time stability criterion of delay‐free system to delay one. Based on these, when the bound of the disturbance is known, we design the corresponding prescribed‐time stabilization and robust stabilization controller. On the basis, we further give several robust stabilization results for the unknown disturbance by designing a new prescribed‐time controller. Finally, the study of two illustrative examples shows that the results obtained of the paper work well in the prescribed‐time robust stabilization the nonlinear delay systems. It is well worth pointing out that, different from existing infinite‐time and finite‐time asymptotical results of nonlinear delay system, the closed‐loop system under the prescribed‐time controller can reach the desired position within the specified time, and has stronger anti‐interference ability. [ABSTRACT FROM AUTHOR]

This paper offers to examine the decomposition of the consumption function of the male and female households in India. The decomposition of consumption function is explained by the socio‐economic and demographic variables. The dataset on the 61st round (2004–2005) and 68th round (2011–2012) of NSSO has been used for the analysis. The ordinary least squares (OLS) method is used to explain the important determinants of household consumption expenditure. Also, the Blinder–Oaxaca (B‐O) decomposition technique is used to estimate and to explain the difference of the per capita consumption expenditure of male and female households. The results find that age, household size, education, wages, occupation, and rural dummy are key determinants of the consumption function of male and female households. The results also found that the impact of education and occupation particularly in the service sector is relatively higher for female households than male households. Moreover, the results from the B‐O decomposition method are found that there is a positive gap of per capita consumption expenditure between male–female households in 2004–2005 and these differences are coming from differences of endowment effect between male and female households. But, the results from the year 2011–2012 data have shown that there is a negative mean difference in consumption expenditure of male and female households and this difference is coming from the differences of coefficient effect between male and female households. Finally, the results concluded that the per capita consumption expenditure gap is explained due to changes in education, occupation, and earnings of households. [ABSTRACT FROM AUTHOR]

Railway track defects must be detected and repaired to ensure the safe operation of the railway. Signals generated by wheelset contact with rail discontinuities contain information about railway track structure and health. Each signal has a characteristic pattern, which is modified in the presence of defects. Signal decomposition is commonly used to extract characteristics from impact response signals. It is necessary to select an appropriate decomposition method for the wheel/rail impact signal. This paper presents a study of the effectiveness of decomposition methods to identify the most appropriate technique for classification of rail conditions. Vibration signals have been generated in experiments and decomposed. The signal characteristics are presented and energy‐based methods are used in an evaluation of the different approaches. Standard deviation is used to assess the similarity of parameter value from different intrinsic mode functions (IMFs); maximum and minimum differences from the mean value are used to consider the effectiveness of each technique in identifying specific damage types. The evaluation shows that variational mode decomposition (VMD) is suitable for the rail application. Classifiers are applied to the outputs of the selected decomposition method. The results demonstrate the suitability of the approach for processing wheel/rail impact response signals to detect faults and improve safety. [ABSTRACT FROM AUTHOR]

In this article, we investigate the existence and uniqueness of solutions to a generalized nonlinear functional integral equation (G‐NLFIE) associated with certain epidemic models of infectious diseases, defined within the Banach space C[0,1]$$ C\left[0,1\right] $$. Our existence results include several specific cases of nonlinear functional integral equations that commonly occur in nonlinear sciences. We then introduce an iterative algorithm that combines Adomian's decomposition method (ADM) with the modified homotopy perturbation method (mHPM) to approximate solutions to the G‐NLFIE. The paper addresses the convergence properties and error analysis of this method. Finally, we present numerical examples to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

ABSTRACT Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR‐ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR‐ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR‐ALS, offers the flexibility to be applied component‐wise or data block‐wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher‐order dataset with whatever pattern of missing values. [ABSTRACT FROM AUTHOR]

The inverted velocity model obtained from the reflection tomography based on the angle domain common‐image gathers has a certain fuzziness. The inverted velocity model's stratigraphic interface is always not clear enough in areas with complex stratigraphic structure. In order to improve the accuracy and resolution of the inverted velocity model, a double‐difference constraint condition is added on the basis of minimizing the absolute travel‐time residual at the subsurface imaging points. This constraint makes the inverted velocity model local structure information more refined by minimizing the differential travel‐time residual at adjacent imaging points (i.e. closely spaced points within the same layer) and makes the variation of velocity model information within a certain range more accurate. The method in this paper is based on angle domain common‐image gathers, the tomography inversion equation is established by using the ray tracing method, and the conversion relationship between the traveltime residual and the residual curvature of the angle domain common‐image gathers. Then, by adding differential constraint and double‐differential constraint conditions and using the least squares QR decomposition method to solve the set of equations, the inverted velocity model can be obtained through multiple iterations, which provides a high‐precision velocity field for the migration and improves the accuracy of seismic imaging. Numerical experiments on both one typical model and a field data example demonstrate the effectiveness of the proposed double‐difference constrained elastic reflection tomography in generating high‐precision velocity models. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to explore economic inequality in 'the Belt and Road' region where the investigation of the economic status is important but remains underdeveloped. The application of night‐time satellite imagery in 2015 can effectively make up for the shortcomings of insufficient data at various scales, especially for developing countries. We explored inequality at a multi‐scale level using the Theil decomposition method. As a result, (1) night‐time light data can present economic development and assess inequality in 'the Belt and Road' region. (2) The between‐sub‐region inequality contributed most to disparity, while the between‐country inequality was the least. Night‐time light data in West Asia and Central and Eastern Europe was higher than that in other sub‐regions. Countries within Central, Southeast and West Asia exhibited high inequalities, indicating an obvious sub‐regional pattern. (3) Both positive and negative impacts of economic growth on inequality are observed, indicating that 'the Belt and Road' countries are in different stages of economic development. Nightlight satellite imagery can effectively monitor social‐economic development and will play an important role in the implementation of 'the Belt and Road' initiative and achieving the sustainable development goals. [ABSTRACT FROM AUTHOR]

Based on a Fairlie decomposition method, this paper analyzes the drivers of gender gaps in financial inclusion in Cameroon. We use Finscope 2017 data for Cameroon and assess six distinct financial inclusion variables grouped into two dimensions mainly, access to and use of financial products and services. Our results show that there is a gap in all indicators of access to and use of financial products and services in favor of men. The results also show that the largest contributor to the gender gap in access to financial products and services is income, with more than 50% of the contribution, whereas the largest contributor to the gender gap in the use of financial products and services is education with an average contribution of more than 35%. Based on the above findings, decision‐makers in Cameroon must conduct economic policies toward facilitating equitable access to education, by providing incentives to attract women to gain higher education. [ABSTRACT FROM AUTHOR]

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Laminated rubber bearings are the key components of base isolated structures. It is important to effectively monitor the axial pressure and shear deformation in rubber bearings. In previous studies, the axial pressure and shear deformation of rubber bearings have been monitored separately using wavelet‐packet‐based method. However, when the axial pressure and deformation of rubber bearings change at the same time, the prediction error of the above method is large. Therefore, this paper proposed a new deep neural network architecture, named P&D‐NET to monitor the axial pressure and shear deformation simultaneously. The P&D‐NET combines wavelet packet decomposition method and fully connected neural network. In this study, the coupling effects of axial pressure and shear deformation monitoring are studied by full‐scale experiments. The details of P&D‐NET are also introduced. Three full‐scale smart rubber bearings (SRB) were tested under different axial pressure and shear deformation to form a real measured dataset. The influence of feature extraction methods, network structures, and weighting coefficient in loss function was investigated to optimize the network. By using the optimal hyperparameters, the root‐mean‐square error (RMSE) of axial pressure prediction and shear deformation prediction in the test set is 1.41 MPa and 17.6%, which has reduced the error by up to 68.8% compared to the previous wavelet‐packet‐based method. [ABSTRACT FROM AUTHOR]

Mean‐reverting portfolios with few assets, but sufficient variance, are of great interest for investors in financial markets. Such portfolios are straightforwardly profitable because they include a small number of assets whose prices not only oscillate predictably around a long‐term mean but also possess enough volatility. Roughly speaking, sparsity minimizes trading costs, volatility provides arbitrage opportunities, and mean‐reversion property equips investors with ideal investment strategies. Finding such appealing portfolios can be formulated as a nonconvex quadratic optimization problem with an additional sparsity constraint. To the best of our knowledge, there is no method in the literature that directly solves this problem with its solution achieving favorable theoretical properties. In this paper, we develop an efficient two‐stage algorithm for solving this problem which, in the worst case, provably obtains a stationary point and further, demonstrates numerical efficiency. In the first stage of our algorithm, we apply a tailored penalty decomposition method for finding a stationary point of this nonconvex problem. For a fixed penalty parameter, the block coordinate descent method is utilized to find a stationary point of the associated penalty subproblem. In the second stage, we improve the result from the first stage via a greedy scheme that solves restricted nonconvex quadratically constrained quadratic programs (QCQPs). We show that the optimal value of such a QCQP can be obtained by solving their semidefinite relaxations. Numerical experiments on S&P 500 are conducted to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a flexible finite‐difference technique for analyzing the nonuniform guiding structures. Because the voltage and current variations along the nonuniform structure differ for each segment, this work considers the adaptable discretization steps. This technique increases the accuracy of the final response. Moreover, by applying the singular value decomposition and discarding the nonprincipal singular values, an optimal lower rank approximation of the discretization matrix is obtained. The computational cost of the introduced method is significantly reduced using the optimal discretization matrix. Also, the proposed method can be extended to the nonuniform waveguides. The technique is verified by analyzing several practical transmission lines and waveguides with nonuniform profiles. [ABSTRACT FROM AUTHOR]

Although binary financial distress prediction (FDP) is widely explored, multiclass FDP (MFDP) lacks enough investigation. This paper designs a new fusion method of improved decision‐directed acyclic graph (IDDAG) based on the principle of maximizing generalization ability and integrates it with the one‐versus‐one (OVO) decomposition method (OVO‐IDDAG) to build MFDP models. Based on data of Chinese listed companies, we categorize corporate financial status into four states (financial soundness, financial pseudosoundness, moderate financial distress, and serious financial distress) and compare the MFDP performance of OVO‐IDDAG with two benchmark methods, that is, the OVO decomposition method integrated with the max‐win fusion method (OVO‐ max‐win) and the OVO decomposition method integrated with the original DDAG fusion method (OVO‐ODDAG). The experimental results show that the OVO‐IDDAG method overall outperformances the benchmark methods. Among the six base classifiers of multivariate discriminant analysis, Logit, naive bayes, support vector machine, decision tree, and random forest and the three imbalance processing mechanisms of random oversampling, random undersampling, and synthetic minority oversampling technique, the combination of the random forest classifier with the imbalance processing mechanism of random undersampling or the support vector machine classifier with the imbalance processing mechanism of random oversampling or synthetic minority oversampling technique is more suitable for MFDP based on OVO‐IDDAG. This study not only improves artificial intelligence methodology for multiclass classification but also enriches applications of intelligent systems to MFDP. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

The current decomposition methods are not suitable for electromechanical product; the smallest decomposition units obtained by these methods are static parts or components, which cannot reflect the characteristics of electromechanical product that 'action determines motion, and motion determines function'. Meta‐action is the smallest action to realize the function of electromechanical product, and it is reasonable to regard meta‐action as the smallest decomposition unit of electromechanical product. Meta‐action unit is the smallest structural unit to ensure the normal operation of meta‐action, and it is also the smallest carrier of electromechanical product quality characteristics. Meta‐action unit modeling technology is the basis of other subsequent research, so it is necessary to study it. In this paper, detailed criteria for meta‐action decomposition and meta‐action unit separation are formulated, and the standardized structural model, symbolized conceptual model, and assembly model of meta‐action unit are also studied, which provide a basis for mechanical, kinematic, and failure mechanism research of electromechanical product. A CNC (computer numerical control) machine tool made in China is taken as an example for meta‐motion decomposition and meta‐action unit modeling, and the results verify the applicability and correctness of the method proposed in this paper. The proposed method is also applicable to other types of electromechanical product. [ABSTRACT FROM AUTHOR]

This paper introduces conditional counterfactual quantile decomposition methods to the literature on tourism consumption, where observed expenditure differentials are directly referred to differences in tourists' targeted utility. Decomposition terms are synthesized with the neoclassical framework of utility‐constrained expenditure minimization, such that their interpretations do not rely on the approximation of tourists' preferences by typical socioeconomic expenditure determinants. Using survey data from an underrepresented sun, sea, and sand destination, we show that differences in tourists' targeted utility prior to the trip translate exclusively into higher quantities demanded of consumption‐related antecedents, as opposed to higher prices paid by high utility‐targeting tourists. The paper suggests that decomposition analysis may guide profit maximization in the tourism industry. [ABSTRACT FROM AUTHOR]

Crushing ratio and fracture conductivity are two key indicators for proppant evaluation. The most common way to evaluate these two indicators is physical testing, but physical testing is costly and time‐consuming, and the results are unstable. Therefore, a method for calculating proppant‐crushing ratio and fracture conductivity is proposed to replace repetitive physical experiments. However, in past attempts, only mathematical models using the same particle size have been established, while the particle‐size distribution in actual proppant arrangement is within a certain numerical range. Therefore, in order to establish a mathematical model more consistent with the actual situation of proppant arrangement, firstly, the Monte Carlo method was used to simulate proppant particles arranged with different particle sizes, and then a mathematical model was established describing the force of proppant particles. Secondly, according to the sparse, ill‐conditioned, and asymmetric characteristics of the coefficient matrix, an improved singular value decomposition method is proposed to improve the accuracy and efficiency of calculation. Finally, the crushing ratio and fracture conductivity of proppant were calculated through the new model in this paper. It was found that the results calculated by the new model in this paper are closer to actual test results. [ABSTRACT FROM AUTHOR]

In this paper, the Lagrange stability (LS) for quaternion memristive neural networks with time delay is taken into consideration. On the strength of Lyapunov function, conclusions are derived such that the resultant system is stable in Lagrange sense. The analysis procedure adopts properties of quaternions instead of conventional decomposition method. The nonlinear scalarization approach is further exploited to compare the size of the quaternion, which makes the convex closures formed by quaternions more noteworthy. [ABSTRACT FROM AUTHOR]

As a kind of commutative and associative four-dimensional algebra, commutative quaternion has better applications in color image and signal processing than quaternion. Matrix decomposition is of great concern in the theoretical study and numerical calculation of commutative quaternion. Two kinds of disadvantages of commutative quaternion make the decomposition of commutative quaternion matrix extremely difficult. On one hand, commutative quaternion is not a kind of complete four dimensional division algebra because of the zero divisors. On the other hand, computing the inverse of commutative quaternion is very complicated. In this paper, the semi-tensor product (STP) of matrices will be used to overcome the above two kinds of shortcomings. And we will propose a real structure-preserving algorithm based on STP of matrices for commutative quaternion LU decomposition, which makes full use of high-level operations, relation of operations between commutative quaternion matrices and their L-representation matrices. Numerical experiments will be provided to demonstrate the efficiency of the real structure-preserving algorithm based on STP of matrices. Meanwhile, we will apply the structure-preserving algorithm for strict image authentication. [ABSTRACT FROM AUTHOR]

Summary: The reduced‐order observer‐based finite‐time control problem for one‐sided Lipschitz nonlinear switched singular systems is addressed in this paper. First, the design method of the reduced‐order observer is given via state transformation. Then, based on the average dwell time (ADT) approach, some new sufficient conditions for regularity, impulse‐freeness, have a unique solution and finite‐time boundedness (FTB) of the dynamic augmented systems are obtained by exploring the reduced‐order observer‐based controller. Further, the lower finite‐time bound can be obtained by using singular value decomposition method. And the state feedback gain and the observer gain are computed by solving linear matrix inequalities (LMIs). Finally, the validity of the obtained method is illustrated by means of a numerical example and a DC motor system. [ABSTRACT FROM AUTHOR]

Tensor ring (TR) decomposition has been widely applied as an effective approach in a variety of applications to discover the hidden low‐rank patterns in multidimensional and higher‐order data. A well‐known method for TR decomposition is the alternating least squares (ALS). However, solving the ALS subproblems often suffers from high cost issue, especially for large‐scale tensors. In this paper, we provide two strategies to tackle this issue and design three ALS‐based algorithms. Specifically, the first strategy is used to simplify the calculation of the coefficient matrices of the normal equations for the ALS subproblems, which takes full advantage of the structure of the coefficient matrices of the subproblems and hence makes the corresponding algorithm perform much better than the regular ALS method in terms of computing time. The second strategy is to stabilize the ALS subproblems by QR factorizations on TR‐cores, and hence the corresponding algorithms are more numerically stable compared with our first algorithm. Extensive numerical experiments on synthetic and real data are given to illustrate and confirm the above results. In addition, we also present the complexity analyses of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy. [ABSTRACT FROM AUTHOR]

Catalogs of refactoring have key importance in software maintenance and evolution, since developers rely on such documents to understand and perform refactoring operations. Furthermore, these catalogs constitute a reference guide for communication between practitioners since they standardize a common refactoring vocabulary. Fowler's book describes the most popular catalog of refactorings, which documents single and well‐known refactoring operations. However, sometimes, refactorings are composite transformations, that is, a sequence of refactorings is performed over a given program element. For example, a sequence of Extract Method operations (a single refactoring) can be performed over the same method, in one or in multiple commits, to simplify its implementation, therefore, leading to a Method Decomposition operation (a composite refactoring). In this paper, we propose and document a catalog with eight composite refactorings. We also implement a set of scripts to mine composite refactorings by preprocessing the results of refactoring detection tools. Using such scripts, we search for composites in a representative refactoring oracle with hundreds of confirmed single refactoring operations. Next, to complement this first study, we also search for composites in the full history of 10 well‐known open‐source projects. We characterize the detected composite refactorings, under dimensions such as size and location. We conclude by addressing the applications and implications of the proposed catalog. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to improve dynamic performance of a multi‐terminal DC (MTDC) grid in a wind‐integrated AC power system through DC voltage control. The proposed approach is based on design of robust H∞ controller and loop shaping along with mutual quality decomposition method (MQDM). For extracting transfer function of the plant, model of Master‐Slave control system is converted to standard feedback control system. Then, based on the obtained difference between singular‐value curves of the main open‐loop system and the proposed method, the weighting functions of robust H∞ controller are selected. In the next step, by using the MQDM technique, the transfer function ofthe system in state‐space form is evaluated to extract gain of the robust controller. For practicality of the conducted approach, singular‐value order reduction Hankel algorithm along with frequency response analysis have been used for designing the gain of the robust controller. The developed method has been implemented on the MTDC converter in the wind farm side to control the voltage of DC grid. The simulations are accomplished in MATLAB, where by considering different uncertainties in the DC grid, wind unit, and the AC grid, robustness of the proposed approach has been clearly demonstrated. [ABSTRACT FROM AUTHOR]

New Zealanders can cross borders freely, work and live in Australia indefinitely thanks to the Trans‐Tasman Travel Agreement. This paper uses a recently developed decomposition method to decompose the weekly wage gap at various quantiles on the wage distribution between New Zealand‐born (NZ‐born) and Australian‐born workers, and between NZ‐born workers, migrants from other English speaking countries (OESC), and from non‐English speaking countries (NESC) to determine how free and regulated migration influences migrants' performance in the Australian labour market. We found that NZ‐born workers earned higher weekly wages than both Australian‐born and NESC workers but earned lower wages than OESC migrants. Differences in endowment were primarily responsible for the wage gaps between NZ‐born and Australian‐born workers and between NZ‐born and OESC migrants. However, differences in returns to worker and job characteristics are mainly responsible for the wage gap between NZ‐born and NESC migrants. [ABSTRACT FROM AUTHOR]

Decomposition and localization of a supervisor are both reduction methods in distributed supervisory control of discrete‐event systems. Decomposition is employed to reduce the number of events and localization is used to reduce the number of states of local controllers. In decomposition of a supervisor both observation and control scopes are restricted, whereas in localization only control authority is restricted to the corresponding local controller. In this paper, we propose a decomposition method by defining a coparanormality property, and by using the relative observability property of a monolithic supervisor. Coparanormality is a coobservation property defined based on a paranormality property for a set of natural projections. It is shown that each supervisor can be coparanormal, provided a set of appropriate natural projections exist. Moreover, it is proved that relative observability is a sufficient condition to decompose a supervisor. Furthermore, the supervisor localization procedure is generalized to find a set of local controllers for any partition of the controllable events set. The implementation of such local controllers may become easier in industrial systems. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is a technology that enables the creation of complex shapes with advanced structural and functional properties. It has transformed the traditional manufacturing operations into a more flexible and efficient process, reshaping the whole value chain and allowing new levels of product customization. AM is a layer‐by‐layer manufacturing process, in which materials are deposited in each layer to create the object of interest. Due to the layer‐wise nature of the process, anomalies and defects might occur within each layer, across several layers or throughout the whole sample. An accurate and responsive detection strategy that enables the detection of various types of anomalies is essential for ensuring the quality and integrity of the manufactured product. In this paper, a hierarchical in situ process monitoring approach, namely, a three level monitoring strategy, is proposed to detect local, layer‐wise, and sample‐wise anomalies using thermal videos acquired during the manufacturing process. The proposed approach integrates hierarchical low‐rank tensor decomposition methods with statistical monitoring techniques to effectively detect anomalies at different levels, namely, the within‐layer level, the layer level, and the sample level. Simulations are used to evaluate the performance of the method and compare with existing benchmarks. The proposed approach is also applied to thermal videos acquired during the laser powder bed fusion (L‐PBF) process to illustrate its effectiveness in practice. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design of output feedback controllers for discrete‐time (DT) linear systems. New sufficient LMI conditions are derived for designing static H2$$ {H}_2 $$ and H∞$$ {H}_{\infty } $$ controllers using decomposition of an auxiliary matrix. The decomposition facilitates linearization of nonlinear term of reduced size to obtain linear matrix inequality criteria. This leads to less conservative results as shown in the numerical examples. In addition, the proposed static output feedback criteria is also used for designing dynamic output feedback controllers for DT systems. Furthermore, a comparative study is also made for the proposed design method with the results existing in the literature. Finally, a DT static output feedback H∞$$ {H}_{\infty } $$ controller is designed for a quarter‐car suspension system. Simulation results are provided to show the efficacy of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper investigates the feedback stabilization of non‐homogeneous delayed bilinear systems in Hilbert state space. More precisely, under an observability‐like assumption, we prove the exponential and strong stability of the solution by using bounded feedback control. Partial stabilization is discussed as well. The proofs of the main results are based on the decomposition method. The decay estimates of the stabilized solutions are obtained. Finally, some examples are presented. [ABSTRACT FROM AUTHOR]

In this study, we present convergence analysis along with an error estimate for time‐fractional biological population equation in terms of the Caputo derivative using a new technique called the fractional decomposition method (FDM). Further, we present exact solutions to four test problems of nonlinear time‐fractional biological population models to show the accuracy and efficiency of the FDM. This method based on constructing series solutions in a form of rapidly convergent series with easily computable components and without the need of linearization, discretization and perturbations. The results prove that the FDM is very effective and simple for solving fractional partial differential equations in multi‐dimensional spaces, special cases of which we have described in this paper. [ABSTRACT FROM AUTHOR]

The Levin‐Nohel integro‐differential equation is one of the famous equations in various fields of science and engineering. It was first studied by Volterra in connection with biological applications. This paper deals with the nonlinear neutral Levin‐Nohel integro‐differential equation with Caputo fractional derivative and variable delays. The Ulam‐Hyers‐Rassias stability, Ulam‐Hyers stability, semi‐Ulam‐Hyers‐Rassias stability are studied. The existence and uniqueness of solutions are established by using Krasnoselskii's fixed point theorem and contraction mapping principle. An illustrative example is also given and that is studied by the help of the Adomian decomposition method. [ABSTRACT FROM AUTHOR]

Summary: Exploring the associations between microRNAs (miRNAs) and diseases can identify potential disease features. Prediction of miRNA‐skin cancer associations has become an effective method for detecting skin cancer, as direct detection of skin cancer is challenging. However, traditional binary associations prediction methods overlook high‐order feature differences of miRNAs in different types of skin cancer. Although current tensor decomposition methods have addressed this, they assume a consistent composition standard of global tensor elements, ignoring differences in the composition standards of local sub‐tensors. This leads to poor prediction accuracy and comprehensiveness and fails to consider the credibility and diversity requirements of patients and doctors in practical applications. In this paper, we represent miRNA‐skin cancer associations as a tensor and employ tensor decomposition for tensor completion to achieve prediction purposes. First, we propose a credibility evaluation indicator and introduce four objective functions: accuracy, comprehensiveness, credibility, and diversity, to construct a many‐objective local tensor factorization model (MOLTF). Then, to avoid wrong individuals when solving this model with genetic algorithms, we propose a corrected single‐point crossover operator and a corrected multi‐point mutation operator. On the miRNA‐disease dataset HMDD v3.2, our algorithm improves accuracy by 14.5% compared to the recent baseline, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

The possibilities offered by the use of variational mode decomposition–based Hilbert marginal spectrum and the differential cepstrum for gas‐bearing detection are studied in this paper. We propose a novel variational mode decomposition–based Hilbert marginal differential cepstrum for hydrocarbon detection. Variational mode decomposition–based Hilbert marginal spectrum is first computed. Then discrete cosine transform is carried out to the differential logarithmic variational mode decomposition–based Hilbert marginal spectrum to obtain the variational mode decomposition–based Hilbert marginal differential cepstrum. For hydrocarbon detection, the seismic amplitude anomaly section is generated by extracting the first and second common quefrency sections. Compared with the traditional Fourier‐based cepstrum, the wavelet‐based cepstrum and the Berthil cepstrum, it has the ability to effectively reveal more detailed frequency‐dependent amplitude anomalies with high accuracy and resolution. Model tests and field data applications from a carbonate reservoir in China show that the variational mode decomposition‐based Hilbert marginal differential cepstrum can provide a better gas‐prone interpretation. The proposed method can be a complementary approach to current cepstrum‐based hydrocarbon detection methods and the spectrum decomposition methods. [ABSTRACT FROM AUTHOR]

A linear forest is a disjoint union of path graphs. The linear arboricity of a graphG $G$, denoted by la(G) $\text{la}(G)$, is the least number of linear forests into which the graph can be partitioned. Clearly, la(G)≥⌈Δ(G)∕2⌉ $\text{la}(G)\ge \lceil {\rm{\Delta }}(G)\unicode{x02215}2\rceil $ for any graph of maximum degree Δ(G) ${\rm{\Delta }}(G)$. For the upper bound, the long‐standing Linear Arboricity Conjecture (LAC) due to Akiyama, Exoo, and Harary from 1981 asserts that la(G)≤⌈(Δ(G)+1)∕2⌉ $\text{la}(G)\le \lceil ({\rm{\Delta }}(G)+1)\unicode{x02215}2\rceil $. A graph is a pseudoforest if each of its components contains at most one cycle. In this paper, we prove that the union of any two pseudoforests of maximum degree up to 3 can be decomposed into three linear forests. Combining it with a recent result of Wdowinski on the minimum number of pseudoforests into which a graph can be decomposed, we prove that the LAC holds for the following simple graph classes: k $k$‐degenerate graphs with maximum degree Δ≥3k−1 ${\rm{\Delta }}\ge 3k-1$, all graphs on nonnegative Euler characteristic surfaces provided the maximum degree Δ≠7 ${\rm{\Delta }}\ne 7$, and graphs on negative Euler characteristic ϵ $\epsilon $ surfaces provided the maximum degree Δ≥35+49−24ϵ4−1 ${\rm{\Delta }}\ge 3\unicode{x02308}\frac{5+\sqrt{49-24\epsilon }}{4}\unicode{x02309}-1$, as well as graphs with no Kt ${K}_{t}$‐minor satisfying some conditions on maximum degrees. [ABSTRACT FROM AUTHOR]

This paper investigates distributed cooperative learning event‐triggered control for discrete‐time strict‐feedback nonlinear multi‐agent systems with an identical system structure and different recurrent reference orbits. A clever system decomposition method is firstly proposed to divide every n$$ n $$‐order agent system into n$$ n $$ first‐order subsystems, which makes it possible to solve the tough problem that every estimated neural weight cannot converge to an unique ideal value based on the existing control scheme. By constructing two interaction typologies, a novel distributed cooperative weight updating law is designed by the introduction of the internal interaction terms between n$$ n $$ subsystems of every agent, the external interaction terms between agents, and the triggering mechanism between agents. With the combination of the graph multiplication operation, the consensus theory and the matrix null space property, the proposed cooperative event‐triggered control scheme guarantees that every agent can track their corresponding recurrent reference trajectories. And further the exponential convergence of all agents' neural estimated weights to a close vicinity around their mutual and unique ideal weights is verified under the condition that the directed interaction topology between agents is strongly connected and balanced. Such a weight convergence makes the proposed scheme has some significant advantages including the small data storage space, the convenient knowledge reuse, the good generalization ability, and the low communication burden. By reinvoking the saved constant weights, a static learning controller is presented for the high‐performance control of similar diversified tracking tasks. Simulation studies and some comparisons are given to show the advantages and effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

This paper presents a new formulation for solving the expansion planning of transmission lines and energy storage systems while considering the integration of electricity and gas networks. The proposed model is a bi‐level stochastic planning model. It involves transmission and battery expansion planning at one level, and gas network modeling at the other. The study addresses the impact of high penetration of renewable resources and security constraints on both the electricity and gas networks within the context of network expansion planning. The proposed model is a stochastic mixed‐integer linear programming model at both levels, and its challenging solution is achieved through reformulation and decomposition methods. Two experimental networks are analyzed: a 6‐node network and the IEEE RTS 24‐bus network for the electricity network, coupled with 5‐node and 10‐node gas network systems. The results demonstrate the efficiency of the proposed model. Simulation results indicate that the proposed model is highly effective in scenarios where power and gas network lines are disconnected, preventing load shedding even when the integrated network lines are disconnected. [ABSTRACT FROM AUTHOR]

In this paper, new fast computing methods for partial differential equations with variable coefficients are studied and analyzed. They are two kinds of two‐sided Krylov enhanced proper orthogonal decomposition (KPOD) methods. First, the spatial discrete scheme of an advection‐diffusion equation is obtained by Galerkin approximation. Then, an algorithm based on a two‐sided KPOD approach involving the block Arnoldi and block Lanczos processes for the obtained time‐varying equations is put forward. Moreover, another type of two‐sided KPOD algorithm based on Laguerre orthogonal polynomials in frequency domain is provided. For the two kinds of two‐sided KPOD methods, we present a theoretical analysis for the moment matching of the discrete time‐invariant transfer function in time domain and give the error bound caused by the reduced‐order projection between the Galerkin finite element solution and the approximate solution of the two‐sided KPOD method. Finally, the feasibility of four two‐sided KPOD algorithms is verified by several numerical results with different inputs and setting parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal tracking performance based on the codec, colored noise, and bandwidth constraints in the network time‐delay systems. Choosing the optimal two‐degree‐of‐freedom compensator and the dominant expression of the system optimal tracking performance is obtained after Youla parameterization by using the coprime factorizations method and spectral decomposition technology. The result shows that the tracking performance of the system is determined by the nonminimum phase zero and unstable pole of the controlled plant. Moreover, codec, colored noise, and bandwidth constraints can also significantly impact the performance. Finally, two simulation examples prove the accuracy of the theory. [ABSTRACT FROM AUTHOR]

Infrared (IR) and visible (VI) image fusion play an important role in improving the sea scene perception and ship target detection. Although there have been many studies on image fusion considering the sea scene characteristics, there has still been no adequate extraction method for detailed information of a ship target in a sea scene. To overcome this shortcoming, this paper proposes a fusion method based on a scale‐aware feature decomposition, which can accurately extract features of a ship target at different scales. First, a hybrid feature decomposition method based on the scale‐aware structure‐preserving filter and Gaussian filter is designed. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer‐scale division than traditional multiscale decomposition methods in the sea‐clutter background. Then, further decomposition of the texture layer was performed by the shearlet transform to obtain the directional texture feature. According to the characteristics of each layer, different strategies were adopted for the fusion, and the weighting factor was used to obtain the final fusion result. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state‐of‐the‐art methods. This method introduces a scale‐aware edge preserving filter to improve the ability of detail extraction and is suitable for image fusion of ship targets. [ABSTRACT FROM AUTHOR]

Submersible motors are widely used in industry, agriculture, and life. Despite their safety and efficiency, submersible motors suffer from a harsh working environment. Overheating and overload may cause seal damage to make sediment flood in, which will lead to bearing faults. Meanwhile, due to the particularity of the working environment of submersible motors, the traditional method of detecting bearing faults through vibration signals is challenging to implement. In this paper, a numerical model based on the eccentricity analysis method is proposed to simulate the bearing faults process of submersible induction motors so that electrical signals, including air gap flux density and exciting force, could be obtained to diagnose the bearing faults. The proposed approach is capable of approximately simulating the motion orbit of the rotor origin when a single point fault occurs. Then, the 2D Fourier decomposition method is used to analyze the influence of rotor deflection on air gap flux density and exciting electromagnetic force from order and frequency. Finally, the proposed model is tested and proved efficient by analyzing the emerging fault harmonic components. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]