Decomposition Results Research Articles

Nonferrous metal price forecasting based on signal decomposition and ensemble learning

Nonferrous metals are indispensable raw materials for modern industry. The price forecasting of nonferrous metals is vital for business operators and investors. Based on the decomposition-integration framework, we propose a signal decomposition model combining variational mode decomposition (VMD) and an improved long-short time memory (LSTM) network. Using the MAE metric as a benchmark, the improved LSTM model (Mogrifier LSTM) obtained an average accuracy improvement of 5.99%. VMD is an efficient decomposition algorithm. However, it needs to set hyperparameters in advance. Unreasonable parameters will lead to poor decomposition results. Therefore, a method based on subseries complexity and reconstruction error (CAE) is proposed to reasonably decompose signals, improving 21.13% accuracy and reducing 37.56% computational overhead than other strategies. The structural model is introduced as a complement to the signal decomposition model, which learns different features by incorporating theoretical analyses into the choice of explanatory variables. The combining of two models achieves effective complementarity, obtaining an average accuracy improvement of 7.43%. Comparative tests on three datasets demonstrate the superiority of the proposed prediction framework. On the one hand, a reasonable decomposition strategy can play an essential role in the signal decomposition model. On the other hand, improving the prediction model and integrating different models is also an effective strategy to enhance accuracy.

Research on non-stationary characteristic test and decomposition for dynamic response of floating structures

Considering the complex influences of harsh marine environments, auxiliary structures, and various operational processes, motion response of floating structures will inevitably exhibit non-stationary behaviors. Therefore, grasping the non-stationary characteristics of motion responses is imperative for a comprehensive understanding of the operational mechanisms. This paper presents a strategy for testing non-stationary characteristics and decomposing dynamic responses, with the objective of investigating the influence of non-stationary characteristics on motion responses of floating structures. The primary contributions of this study are: (1) Proposed a cointegration analysis method for characteristics testing applicable to floating structures to investigate potential non-stationary variations. It explains the statistics of non-stationary characteristics through dimensional analysis, establishing a connection between the test statistics and the engineering intuition of non-stationary motion response. (2) Proposed a non-stationary signal decomposition method based on adaptive segmentation and decomposition control techniques to overcome mode mixing issues in the decomposition of structural motion responses. The validity and applicability of the developed strategy for testing and decomposing non-stationary responses are verified through a synthesized signal and a numerical semi-submersible platform. Numerical results demonstrate that this strategy can effectively test the non-stationarity, and the accuracy of decomposition results is greater than 99%, and the maximum normalization error is less than 2%. Finally, physical experiments are conducted on a semi-submersible platform under regular and irregular waves action, respectively, to verify the feasibility of the developed strategy when applied to measured data. Experimental results indicate that this strategy can effectively detect the non-stationary characteristics of the measured response, and realize an accurate decomposition of structural motion responses. These results will provide valuable insights into understanding non-stationary characteristics relevant to various engineering applications.

The driving factors and policy evaluation of China’s carbon efficiency from the perspective of the enterprise heterogeneity

The identification of factors that drive enterprise carbon efficiency and the assessment of current emission reduction policies from the perspective of enterprise heterogeneity are essential for designing more effective emission reduction policies that optimize the allocation of inter-enterprise resources. Using a panel of 602,470 observations of Chinese industrial enterprises over multiple years, we quantified the factors driving carbon efficiency and their contributions. We also examined the heterogeneity of these effects at the industry and regional levels. This was done by constructing multiple fixed effect models and the Shapley value decomposition model. Additionally, we evaluated carbon reduction policies related to the key driving factors, taking into account the heterogeneity of the enterprise. The study discovered significant variations in inter-enterprise carbon efficiency, with a trend of local leadership and overall trailing. Based on the results of Shapley value decomposition, industry differences, enterprise scale, and regional differences are the most critical factors affecting enterprise carbon efficiency. Based on the results of the t-test and robustness test, enterprise scale has a significant inverted “U”-shaped impact on that. The degree of impact is greater in the eastern region and related industries in China with higher levels of market development, environmental regulation, and carbon market penetration. Previous policies have overlooked the heterogeneity of carbon efficiency among enterprises, resulting in firms and regions with large production scales and high carbon efficiency shouldering a greater burden of emission reduction tasks. It is likely to encourage the flow of output away from enterprises or areas with high carbon efficiency and towards those with low carbon emissions, which hinders the improvement of overall carbon efficiency. The conclusions have valuable policy implications. These include the recommendation to reduce direct control of total emission reduction targets in carbon reduction policies. Instead, based on the inter-enterprise heterogeneity of carbon efficiency, the policies should focus on strengthening economic measures and developing specific carbon emission reduction policies at the provincial, industry, and enterprise levels.

The Relationship Between Domestic Credit Volume and Unemployment Rate In The Turkish Economy: An Empirical Analysis

In this study, monthly frequency data covering the period between 2014:01 and 2022:10 were used to examine the relationship between domestic credit volume and unemployment rate in the Turkish economy. For this purpose, Vector Autoregressive Model (VAR) was used for the analysis and impact-response function and variance decomposition analyses were performed to interpret the coefficients obtained. According to the impact-response function results, shocks originating from both variables had a negative effect for a certain period and then created a positive effect before damping out. In addition, according to the variance decomposition results, approximately 85.2% of the change in domestic credit volume was explained by itself after 15 months, while approximately 14.79% was explained by the unemployment rate. Similarly, after 15 months, approximately 92.54% of the changes in the unemployment rate were explained by itself, while 7.45% was explained by domestic credit volume. Overall, it was determined that the effects of each variable on the total variability of the other variable lasted for 9 and 6 months, respectively. Finally, the Granger causality test was applied to reveal the causal relationships between domestic credit volume and unemployment rate. According to the test results, it was determined that the unemployment rate is the Granger cause of domestic credit volume. Therefore, it can be said that changes in the unemployment rate create changes in domestic credit volume.

Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model

This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance forecasting capabilities. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the accurac by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

Multi-cluster high performance computing method based on multimodal tensor in enterprise resource planning system

The big data representation and processing method based on multimodal tensors can achieve the fusion representation of different types of data, and perform correlation analysis on the fusion data through multimodal tensor decomposition, providing accurate data services for practical applications. However, in the process of big data analysis and processing based on multimodal tensors, there are still some urgent problems to be solved, such as repeated calculations caused by streaming data, time-consuming calculations caused by large scale, ultra-high energy consumption caused by complex calculations, and poor analysis results caused by complex relationships. This article uses big data tools Hadoop and Spark to build a data mining platform to analyze and study the data of traditional manufacturing enterprises' enterprise resource planning (ERP) systems. In order to improve the accuracy of data analysis, the Stacking fusion framework was also introduced. Furthermore, We transform the data generated by the enterprise into valuable information, making more accurate decisions for enterprise personnel. This article proposes a distributed parallel multi-mode tensor chain decomposition method. In the distributed parallel multi-mode tensor chain decomposition method, a method and theoretical proof for solving tensor chain decomposition are proposed based on the original tensor chain decomposition results and tensor chain decomposition results. The multi kernel clustering algorithm based on K-Means aims to improve the performance of kernel K-clustering by integrating a set of kernels. Deep learning model is also proposed to fuse these multilinear attributes, a clustering method based on weighted average similarity matrix and a clustering method based on optional weighted Euclidean distance were proposed. The distributed parallel multimodal method proposed in this article can effectively achieve tensor chain decomposition, thereby improving the efficiency of big data processing. © 2001 Elsevier Science. All rights reserved

Design of sinusoidal-shaped inlet duct for acoustic mode modulation noise reduction of cooling fan

The noise reduction method of installing a sinusoidal-shaped inlet duct on a cooling fan is analyzed theoretically and experimentally in terms of the acoustic mode modulation. Based on Lowson's point force model, the correlation between the free field noise and acoustic mode of the fan rotor and the unsteady forces on the rotor blade surface is established. The azimuthal acoustic mode is demonstrated to partially reflect the composition and intensity of the acoustic source. The theoretical variation in azimuthal acoustic mode indicates that it is possible to completely cancel each mode simultaneously when the phase differences of forces of each order in the secondary source are controlled. A sinusoidal-shaped inlet duct noise reduction structure controlled by a parameter equation is proposed, and the preferred parameter selection is given by the far-field noise measurements. The results of azimuthal mode decomposition exhibit a similar trend to the theoretical analysis. Compared with the straight duct, the chosen sinusoidal-shaped inlet duct achieved greater noise reduction at the blade passing frequency and its first harmonic. The average total sound pressure level in the far field was 6.0 dBA lower than that of the prototype fan, and 1.4 dBA lower than that of the straight duct model.

HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model

Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper proposes a load forecasting model CEEMDAN-Conv1D-BiLSTM-AM which combines empirical mode decomposition and neural networks. The load data are decomposed into fifteen sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The neural network inputs consist of the decomposition results and five exogenous variables. The neural networks contain a one-dimensional convolutional layer, a BiLSTM layer, and an attention mechanism layer. The Conv1D is employed to extract deep features from each input variable, while BiLSTM and the attention mechanism layer are used to learn the characteristics of the load time series. The five exogenous variables are selected based on the correlation analysis between external factors and load series, and the number of input steps for the model is determined through autocorrelation analysis of the load series. The performance of CEEMDAN-Conv1D-BiLSTM-AM is compared with that of five other models and the results show that the proposed model has a higher prediction accuracy than other models.

A depth iterative illumination estimation network for low-light image enhancement based on retinex theory

Existing low-light image enhancement techniques face challenges in achieving high visual quality and computational efficiency, as well as in effectively removing noise and adjusting illumination in extremely dark scenes. To address these problems, in this paper, we propose an illumination enhancement network based on Retinex theory for fast and accurate brightening of images in low-illumination scenes. Two learning-based networks are carefully constructed: decomposition network and enhancement network. The decomposition network is responsible for decomposing the low-light input image into the initial reflectance and illumination map. The enhanced network includes two sub-modules: the illumination enhancement module and the reflection denoising module, which are used for efficient brightness enhancement and accurate reflectance. Specially, we have established a cascaded iterative lighting learning process and utilized weight sharing to conduct accurate illumination estimation. Additionally, unsupervised training losses are defined to improve the generalization ability of the model. The proposed illumination enhancement framework enables noise suppression and detail preservation of the final decomposition results. To establish the efficacy and superiority of the model, on the widely applicable LOL dataset, our approach achieves a significant 9.16% increase in PSNR compared to the classical Retinex-Net, and a remarkable enhancement of 19.26% compared to the latest SCI method.

Development Level and Growth Source of Chinas Digital Inclusive Finance An Investigation Based on Contribution Decomposition Model

The outbreak of COVID-19 epidemic not only highlights the solid foundation of China's digital economy, but also counterforces and accelerates its further development, which provides a potential opportunity for China's economic transformation and upgrading in the next stage. This paper uses the contribution decomposition model, examines the development level and growth sources of Chinas digital inclusive finance from three dimensions: the coverage, the using depth and the digitalization degree. The research shows that from 2011 to 2020, the overall level and each dimension indexes of digital inclusive finance development show a leap-forward development, but the problem of insufficient and imbalance of regional financial development is still quite obvious. The results of contribution decomposition show that, taking 2016 as the boundary, the growth source of digital inclusive finance index in the former stage lies in the coverage, and the latter stage mainly depends on the coverage and using depth of digital finance, while the emphasis on the digitalization degree of digital finance is relatively insufficient. Under the background of the complex situation and severe challenges in the post-epidemic era, efforts should be made in the top-level design and elements support, aiming at the regional and dimensional shortcomings, so as to make positive contributions to the high-quality development of China's economy.

Oil Shocks, Monetary Policy, and Stock Returns: A Case of Oil-based Economy

This paper investigates the dynamic effects of oil price shocks on the Saudi stock market. It differs from previous work by examining the role of monetary transmission of oil shocks to stock returns via interest rate and credit channels. A Structural Vector Autoregressive model (SVAR) is used to trace the impacts of the underlying shocks. The results provide robust evidence in favor of a positive relationship between oil price volatility and stock market returns, which is aligned with the prevailing view on the oil impact in net oil-exporting countries. Variance decomposition results indicate that oil shocks are the key source of fluctuations in stock markets as they explain more than 25% of stock return variations. On the other hand, a weak over-all effect of monetary policy is concluded by this study using the two monetary policy channels. Unlike interest rate, credit channel is found to be relatively effective in the monetary policy transmission to stock market. The response of stock returns showed a positive significant impact of credit shocks, but a negligible effect of interest rate shocks. The outcome of this paper is considered plausible in an oil-based economy with fixed-exchange rate regime.

Bidimensional local characteristic-scale decomposition and its application in gear surface defect detection

Visual image-based inspection methods can directly reflect the type of defects on the surface of gears. However, these methods have many problems: firstly, as a two-dimensional signal, the data volume of images is large and the processing is relatively time-consuming. Although some existing image signal processing methods (e.g. bidimensional empirical mode decomposition (BEMD)) have good decomposition results, their decomposition speed is slow. The bidimensional local characteristic-scale decomposition (BLCD) method is proposed in this paper, which adaptively decomposes an image from high to low frequencies into several bidimensional intrinsic scale components. It is demonstrated that the BLCD method maintains the advantages of the BEMD method in terms of good decomposition ability and adaptive capability while significantly reducing the processing time and improving the computational efficiency. Secondly, in the running state of the gears, the obtained images sometimes contain noise, which is not convenient for detecting surface defect types. A gear surface defect detection method based on BLCD image denoising is proposed in this paper. Firstly, it uses the BLCD denoising module for preprocessing to provide high signal-to-noise ratio images for the subsequent detection module, and then uses the detection module for defect identification and classification. Experiments prove that the BLCD denoising module has excellent performance and it is well coupled with the detection module, giving the whole method higher accuracy and stability than other classification methods.

Fault diagnosis method of weak vibration signal based on improved VMD and MCKD

During the manufacturing process of electronic equipment, objects such as tin beads and glue blocks may be left in the electronic equipment, causing failure of the electronic equipment. This paper uses experimental equipment to collect weak vibration signals on the surface of electronic equipment. In view of the nonlinear and non-stationary characteristics of the vibration signal and its easy to be masked by strong background noise, a fault diagnosis method of weak vibration signal based on improved variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD) is proposed. Cosine factors and adaptive weights are introduced to improve the convergence accuracy of the Whale Optimization Algorithm (WOA). The envelope spectrum peak factor is used as the adaptability function of the improved whale algorithm (IWOA) to optimize the parameters of VMD and MCKD. Firstly, based on the decomposition results of weak fault signals by IWOA-VMD, the optimal modal components are selected. Secondly, the fault impact component in the optimal modal component is enhanced based on the IWOA-MCKD algorithm. Finally, the fault characteristic frequency is extracted through the envelope spectrum. The feasibility and superiority of the proposed optimization method are verified through simulation signal analysis and actual case study.

Multiscale hierarchical decomposition methods for ill-posed problems

The multiscale hierarchical decomposition method (MHDM) was introduced in Tadmor et al (2004 Multiscale Model. Simul. 2 554–79; 2008 Commun. Math. Sci. 6 281–307) as an iterative method for total variation (TV) regularization, with the aim of recovering details at various scales from images corrupted by additive or multiplicative noise. Given its success beyond image restoration, we extend the MHDM iterates in order to solve larger classes of linear ill-posed problems in Banach spaces. Thus, we define the MHDM for more general convex or even nonconvex penalties, and provide convergence results for the data fidelity term. We also propose a flexible version of the method using adaptive convex functionals for regularization, and show an interesting multiscale decomposition of the data. This decomposition result is highlighted for the Bregman iteration method that can be expressed as an adaptive MHDM. Furthermore, we state necessary and sufficient conditions when the MHDM iteration agrees with the variational Tikhonov regularization, which is the case, for instance, for one-dimensional TV denoising. Finally, we investigate several particular instances and perform numerical experiments that point out the robust behavior of the MHDM.

Socioeconomic inequality in self-rated health and its determinants: an Oaxaca blinder decomposition in Ilam, West of Iran during 2023

AimTo determine inequality and decompose it’s in Self-Rated Health (SRH).MethodThis population-based cross-sectional study was undertaken on the entire population of the city of Ilam, Iran, in 2023. Multi-stage stratified cluster random sampling with proportion-to-size approach was used to select the participants. Oaxaca-Blinder decomposition technique was used to show the amount of inequity in SRH and to decompose of the gap of SRH between the poor and the rich group of participants.Results1370 persons participated in the study. The 59.38% of participants stated good SRH status and just 8.86% of participants had poor SRH status. The results of the Oaxaca-Blinder decomposition revealed a considerable gap (15.87%) in the poor status of SRH between the rich and the poor. A large proportion (89.66%) of this difference was described by explained portion of the model. The results of decomposition showed that economic status was directly responsible for explaining 27.98% of overall inequality gap between rich and poor people. Moreover, hopelessness to future (32.64%), having an underlying disease (18.34%) and difference in the education level (10.71%) were associated with an increase in inequality disfavoring the poor.ConclusionFor people suffering from underlying disease, it is suggested to devise policies to improve access to/and remove healthcare utilization barriers. To address hopelessness to future, it is recommended to carry out further studies to reveal factors which affect it in more details. This can help policy makers to formulate more realistic and evidence-informed policies on order to lessen the current socioeconomic inequity in SRH.

Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model

Improving product quality and service life by diagnosing defects in compressors through vibration signal analysis of the compressor's shell is challenging due to strong noise interference and limited labeled samples. The paper proposed a semi-supervised diagnosis method based on the convolution Transformer autoencoder (CTAE) for diagnosing hidden defects with small samples and strong noise. Firstly, an optimized variational modal decomposition is utilized to decouple and reduce noise from the raw vibrations, and the decomposition results are concatenated with the vibration signals as input to the model; Secondly, the CTAE is employed to learn the feature distribution of unlabeled samples and to extract and fuse local and global features from the input data; Finally, a labeled samples are used to fine-tune the model and to fuse features from multi-sensor information. The results of using a compressor dataset for validation show that the proposed method has high diagnosis accuracy and robustness with limited labeled samples and different signal-to-noise ratios.

Analisis Indikator Moneter Terhadap Pertumbuhan Ekonomi Di Indonesia

The purpose of this study is to determine the relationship between inflation, interest rates, money supply, and exchange rates on economic growth in Indonesia in the short, medium, and long term. By using an associative/quantitative research approach and secondary data. Data collection techniques are used by means of documentation studies. The method used in this study is Vector Autoregression. The results show that in the short run economic growth is responded to positively by inflation. In the medium term, the inflation variable is responded negatively by economic growth and returns to equilibrium in the long run. The results of variance decomposition show that the variable of economic growth is influenced by the variable itself in the short, and long term. The government, Bank Indonesia is advised to stabilize the inflation rate by implementing an expansionary monetary policy.

Modular stochastic configuration network based prediction model for NOx emissions in municipal solid waste incineration process

The accurate prediction of the nitrogen oxides (NOx) emissions is extremely important for pollutant control in municipal solid waste incineration (MSWI) process. Modular neural network (MNN) provides a support for predicting NOx emissions to overcome the limitations of a single model in nonlinear processes and different climatic conditions. However, the design of sub model for MNN is a challenge. We propose a method based on MNN and adaptive ensemble stochastic configuration network (m-AESCN) in this work. First, fuzzy C-means algorithm decomposes the task into several sub datasets with similar characteristics, and an evaluation function is used to guarantee the optimal decomposition result. Second, for each sub dataset, an adaptive ensemble stochastic configuration network as the sub model of modular neural network. The optimal output for sub model can be obtained by an adaptive weighting method based on variance. Third, in testing phase, a sub model activation method based on cluster centroid is proposed to select a suitable sub model. Besides, the m-AESCN method is validated by the real data of an MSWI plant and shows considerable performance in two different datasets. And the proposed method is compared with several modeling methods, such as stochastic configuration network, support vector regression, random forest and so on. The experimental results under two datasets prove that the maximum improvements in accuracy of m-AESCN are 39.74% and 31.67%, respectively.

Target approach in the formation of future workers’ professional readiness

Continuous updating of labor market requirements for the results of future workers’ professional training actualizes the need to specify the process of forming professional readiness as a key quality of an employee. The purpose of the article is the procedural characteristic of the formation of professional readiness of future workers from the position of the target approach that performs two interrelated functions. The first involves the formulation of a goal that corresponds to a set of certain requirements: terminological unambiguity, conciseness, reality, diagnostics, processuality, concreteness. The second function is characterized by professional and industry specifics. It is provided by the relationship between the ideal goal and the result achievement. The outcome is formed professional readiness of future workers. This quality is diagnosed by criteria and indicators for monitoring its formation. From the target approach point of view, the content vector of the formation of future workers’ professional readiness is expressed in the procedural characteristics of this phenomenon. It is dependent on the main goal of future workers’ professional training, which is decomposed into sub-goals that determine its stages: orientational, operational and activity. The first of them is related to pricing, the objective basis of which is the requirements for labor activity fixed in professional standards. The second one is focused on goal-attainment, which includes the formation of practice-oriented skills of students. The third stage involves reflection by future workers of their own actions, their choice and evaluation from the position of expediency of application in a specific professional situation. One of the means to assess the effectiveness of the process of forming the professional readiness of students is pedagogical monitoring. It is implemented on the basis of criteria and indicators determined by the professional training goals. Accordingly, the result of mastering the orientation stage by future workers and the monitoring criterion is pre – readiness, operational – reproductive readiness, and activity- productive readiness. The results of decomposition of the professional training main goal into sub-goals, the corresponding criteria and indicators of future workers’ readiness formation are presented in a tabular version.

Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition

Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode decomposition (VMD) algorithm. It processes data encompassing multiple channels. Determining the most effective component combination of the penalty parameter (α) and the number of intrinsic mode functions (IMFs) (K) is fundamental and affects the decomposition results. α and K in traditional MVMD are provided by subjective experience. We integrated the particle swarm optimization (PSO) algorithm to iteratively optimize these parameters—specifically, α and K—with high precision. This was then combined with the four quantitative parameters: energy entropy, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and root-mean-square error (RMSE). The RES signal decomposition results were judged, and the most effective component combination for noise suppression was selected. We processed the airborne RES data from the East Antarctic ice sheet using the combined PSO–MVMD method. The results confirmed the quality of the proposed method in attenuating the RES signal noise, enhancing the weak signal of the ice base, and improving the SNR. This combined PSO–MVMD method may help to enhance weak signals in deeper parts of ice sheets and may be an effective tool for RES data interpretation.