In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variatio...
The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so...
With the increasing integration of distributed generation, energy storage, and flexible loads, active distribution networks have gained multiple fault-recovery options, including network reconfiguration, islanded operation, and coordinated source&nda...
Runoff prediction plays a critical role in water resource management and flood mitigation. Traditional runoff prediction methods often rely on single-layer optimization frameworks that process the data without decomposition and employ relatively simp...
Large-scale industrial processes usually adopt centralized control and optimization methods. However, with the growth of the scale of industrial processes leading to increasing computational complexity, the online optimization capability of the doubl...
Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to...
Image fusion technology can process multiple single image data into more reliable and comprehensive data, which play a key role in accurate target recognition and subsequent image processing. In view of the incomplete image decomposition, redundant e...
Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparam...
With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the...
With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optim...
In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decompositi...
Downgoing/upgoing P/S-wave decomposition of ocean-bottom seismic (OBS) multicomponent data can help suppress the water-layer multiples and cross-talks between P- and S-waves, and therefore plays an important role in seismic migration and construction...
Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs). This group of methods decomposes the pre-trained neural network weights through low-rank...
Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that...
Tunnels generally operate underground or underwater in a complex environment. As a result, the health monitoring system is inevitably affected by various environmental factors, which introduces noise to the system. However, the noise contained in the...
Runoff forecasting is of great importance for flood mitigation and power generation plan preparation. To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this researc...
Load forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply. In addition to that...
The decomposition of silicates in sulfuric acid to extract rare earth elements (REE) is typically characterized by the formation of an amorphous silica layer surrounding the receding crystal that may act as a passivation barrier limiting the rate of...
To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation op...
This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition t...
To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tracto...
The characterization of straw decomposition and the resulting carbon (C) and nitrogen (N) release is crucial for understanding the effects of different straw returning methods on the immobilization and cycling of soil organic carbon (SOC) and soil to...
High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet...
Aiming at the problems of modal aliasing and poor noise resistance when processing the vibration acceleration signal of rolling bearings by empirical modal decomposition (EMD), a variational modal decomposition (VMD) method based on parameter optimiz...
Chang’E-7 will be launched around 2026 to explore resources at the lunar south pole. Glaciers are suitable scenes on the earth for lunar penetrating radar verification. In the verification experiment, ice-penetrating signals are inevitably poll...
The fault diagnosis classification method based on wavelet decomposition and weighted permutation entropy (WPE) by the extreme learning machine (ELM) is proposed to address the complexity and non-smoothness of rolling bearing vibration signals. The w...
Accurate characterization of coherent vortex structures in high-speed turbulent boundary layers presents a persistent challenge due to the flow’s high dimensionality and nonlinear dynamics. This study investigates an optimized decomposition fra...
Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a u...
Aiming at the problem that the traditional physical layer technology cannot realize secure transmission due to the large number of users and wide dispersion in the multicast system, a layered transmission method is proposed, and a scheme for the secu...
For liquid hydrogen transportation, thermal insulation materials that are lightweight, compact and exhibit high-performance have been pursued for several decades, and variable density multi-layer insulation (VD-MLI) has been regarded as a promising c...
This paper presents experimental validation of a distributed optimization-based voltage control system. The dual-decomposition method is used in this paper to solve the voltage optimization problem in a fully distributed way. Device-to-device communi...
In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. Thi...
Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper dev...
As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issu...
Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of dec...
The problem of shock wave/boundary layer interaction induced by compression corners widely exists in the external and internal flows of various supersonic/hypersonic aircraft. In practical engineering applications, multistage continuous compression i...
Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price ser...
Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KA...
A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to tak...
This paper proposes a novel rolling bearing fault diagnosis method to address the difficulty of accurate feature extraction from nonlinear and non-stationary vibration signals. First, a Levy–Cauchy Optimized Sparrow Search Algorithm (LOCSSA) is...
Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the ima...
With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is...
Noise is a major challenge for current quantum computers. It can be broadly categorized into bit-flip and phase-flip errors. These two types do not necessarily affect the executed algorithm, and thus the application, in the same way. We illustrate th...
To obtain propellant formulations with superior comprehensive and robustness performance, the study establishes a multi-objective optimization model that accounts for uncertainties. The model adopts a bi-layer structure. The inner layer computes perf...
The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mod...
We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network m...
In order to solve the problems of wind power output volatility and wind power participation in frequency regulation, a method for optimizing the capacity allocation of wind farm storage batteries based on the dual grouping strategy and considering th...
Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning...
To solve the problem of the low diagnosis rate of early weak faults of rolling bearings, a novel bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and convolutional neural network (CNN)−Bidirectional Long Short-Term M...
In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a...
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