Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a m...
Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arri...
This research focuses on a decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach that is proposed for optimal operations of price-driven demand response (PDDR) and PDDR-synergized with the renewable and energy storage dispatch (PDDR-...
An accurate and stable prediction of the corrosion rate of natural gas pipelines has a major impact on pipeline material selection, inhibitor filling process, and maintenance schedules. At present, corrosion data are impacted by non-linearity and noi...
This paper studies a class of attack behavior in which adversaries assume the role of initiators, orchestrating and implementing attacks by hiring executors. We examine the dynamics of strategic attacks, modeling the initiator as an attack planner an...
Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whe...
The paper studies a controllable multi-server heterogeneous queueing system where servers operate at different service rates without preemption, i.e., the service times are uninterrupted. The optimal control policy allocates the customers between the...
The optimal power flow (OPF) problem plays an important role in power system operation and control. The problem is nonconvex and NP-hard, hence global optimality is not guaranteed and the complexity grows exponentially with the size of the system. Th...
Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the n...
25 November 2020
Mapping deep neural network (DNN) models onto crossbar-based neuromorphic computing system (NCS) has recently become more popular since it allows us to realize the advantages of DNNs on small computing systems. However, due to the physical limitation...
We use the HHL algorithm to retrieve a quantum state holding the algebraic normal form (ANF) of a Boolean function. Unlike the standard HHL applications, we do not describe the cipher as an exponentially big system of equations. Rather, we perform a...
A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim...
Graphical log-linear models are effective for representing complex structures that emerge from high-dimensional data. It is challenging to fit an appropriate model in the high-dimensional setting and many existing methods rely on a convenient class o...
A mixed signal with several unknown modes is common in the industry and is hard to decompose. Variational Mode Decomposition (VMD) was proposed to decompose a signal into several amplitude-modulated modes in 2014, which overcame the limitations of Em...
The canonical decomposition of a bipartite graph is a new decomposition method that involves three operators: parallel, series, and
An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s im...
We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposabl...
Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes
In this work, a dual refractive index and temperature sensor based on an interferometric system and on the empirical mode decomposition (EMD) algorithm is presented. Here, it is shown that the EMD provides a comprehensive way to analyze and decompose...
The decomposition characteristics of a SF6 gas-insulated medium were used to diagnose the partial discharge (PD) severity in DC gas-insulated equipment (DC-GIE). First, the PD characteristics of the whole process were studied from the initial PD to t...
Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features fo...
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...
The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decom...
Nonlinear active control is very important in many practical applications. Many well-known nonlinear active noise control algorithms may suffer from high computational complexity and low convergence speed, especially in the nonlinear secondary path c...
Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decom...
This paper proposes a fast phase-only beamforming algorithm for frequency diverse array multiple-input multiple-output radar systems. Specifically, we use the Kronecker decomposition to decompose the desired phase-only weight vector into phase-only t...
The non-uniformity of infrared detectors’ readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited ef...
Metagenomic binning is a crucial step in metagenomic research. It can aggregate the genome sequences belonging to the same microbial species into independent bins. Most existing methods ignore the semantic information of contigs and lack effective pr...
Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and h...
Multiple unmanned aerial vehicle (multi-UAV) confrontation is becoming an increasingly important combat mode in intelligent air combat. The confrontation highly relies on the intelligent collaboration and real-time decision-making of the UAVs. Thus,...
The recognition of transient overvoltage characteristics is the premise of disturbance compensation of the transient overvoltage. Based on that, the recognition algorithm of transient overvoltage characteristics based on symmetrical components estima...
The sliding window method is a commonly used data processing in time series forecasting tasks, and determining the appropriate window size is a crucial step in constructing predictive models. However, the current setting of window size parameters is...
Four typical types of artificial defects are designed in conducting the decomposition experiments of SF6 gas to obtain and understand the decomposition characteristics of SF6 gas-insulated medium under different types of negative DC partial discharge...
The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the...
Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode D...
This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low sig...
A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is m...
To improve simulation accuracy and efficiency of probabilistic fatigue life evaluation for turbine rotor, a decomposed collaborative modeling approach is presented. In this approach, the intelligent Kriging modeling (IKM) is firstly proposed by combi...
It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction...
Recently, magnetocardiography (MCG) has attracted increasing attention as a non-invasive and non-contact technique for detecting electrocardioelectric functions. However, the severe background noise makes it difficult to extract information. Variatio...
Developing Arithmetic Word Problem (AWP) -solving algorithms has recently become one of the hottest research areas because it can simultaneously advance general artificial intelligence and the application of AI technology in education. This paper pre...
Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of...
The ability of road vehicles to efficiently execute different sensing tasks varies because of the heterogeneity in their sensing ability and trajectories. Therefore, the data collection sensing task, which requires tempo-spatial sensing data, becomes...
This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, tw...
Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mod...
This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filte...
This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, r...
A large number of permanent magnet synchronous motors (PMSMs) are used to drive coal conveyer belts in coal enterprises. Sensorless energy conservation control has important economic value for these enterprises. The key problem of sensorless energy c...
Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant stakeholders, and can also provide a reference for policy makers. However, the time interval for the CTP is one day, resulting in a relatively small s...
This study proposes a novel parallel denoising and temperature compensation fusion algorithm for MEMS ring gyroscopes. First, the particle swarm optimization (PSO) algorithm is used to optimize the time-varying filter-based empirical mode decompositi...
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