In this paper, we propose a method for compressing the phase component of a full-complex hologram. The JPEG (Joint Photographic Experts Group) Pleno is undergoing standardization for compressing full-complex holograms. If the full-complex hologram is...
The development of high-entropy alloys has been hampered by the challenge of effectively and verifiably predicting phases using predictive methods for functional design. This study validates remarkable phase prediction capability in complex multicomp...
The phase detection of abnormal gait and the prediction of lower-limb angles are key challenges in controlling lower-limb exoskeletons. This study simulated three types of abnormal gaits: scissor gait, foot-drop gait, and staggering gait. To enhance...
Background/Objectives: Closed-loop phase-locked TMS aims to deliver stimulation at targeted EEG phases, but real-time phase prediction remains a practical bottleneck. Timing errors are especially harmful near peaks and troughs, where small offsets ca...
The liquid–liquid two-phase flow and size prediction of slug droplets in flow-focusing microchannels with different downstream orifice sizes were investigated experimentally. Aqueous solution of 50%-glycerol and mineral oil with 4 wt.% surfacta...
Erosion is one of the important reasons for the thickness decrease and perforation of the pipe walls. Understanding the gas–solid two-phase flow pipe erosion mechanism is the basis for monitoring pipe erosion. According to the structural charac...
The comprehensive properties of high-entropy alloys (HEAs) are highly-dependent on their phases. Although a large number of machine learning (ML) algorithms has been successfully applied to the phase prediction of HEAs, the accuracies among different...
Ensuring the safe operation of marine structures requires accurate phase-resolved wave prediction. However, current studies mostly focus on single-model verification and lack a systematic comparison of mainstream architectures under multiple environm...
The decrease of emissions from vehicle operation is connected mainly to the reduction of the car’s body weight. The high strength and good formability of the dual phase steel grades predetermine these to be used in the structural parts of the car’s b...
Realistic predictions of the contribution of the uncertainty sources affecting the quality of the bathymetric measurements prior to a survey is of importance. To this end, models predicting these contributions have been developed. The objective of th...
High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ens...
The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distri...
In the selection and design of ionic liquids (ILs) for various applications, including heat transfer fluids, thermal energy storage materials, fuel cells, and solvents for chemical processes, heat capacity is a key thermodynamic property. While sever...
In order to improve the accuracy of wind power prediction (WPP), we propose a WPP based on multivariate phase space reconstruction (MPSR) and multivariate linear regression (MLR). Firstly, the multivariate time series (TS) are constructed through rea...
The power factor in electrical power systems is of paramount importance because of the influence on the economic cost of energy consumption as well as the power quality requested by the grid. Low power factor affects both electrical consumers and sup...
Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cor...
The productivity prediction of deep coalbed methane (CBM) wells is significantly influenced by gas-water two-phase flow characteristics and seepage parameters of the fracture network. While numerical simulations offer a comprehensive approach, analyt...
The main purpose of this study is to explore a surface roughness prediction model of Gas-Solid Two-Phase Abrasive Flow Machining. In order to achieve the above purpose, an orthogonal experiment was carried out. Q235 steel as processing material and w...
CO2 pipeline transportation is a core link in the CCUS (Carbon Capture, Utilization, and Storage Technology) industry. Ensuring the flow safety of CO2 pipelines under transient conditions is currently a key and challenging issue in industry research....
Capacity prediction is the basis for the optimization of oil and gas well production work systems and parameter optimization design. Horizontal wells are becoming increasingly popular for oil and gas extraction. However, the seepage law of reservoirs...
Blade wear is the critical problem in the operation of multiphase pump. This paper presents a numerical study of the multiphase flow of multiphase pump. The trajectory of particles in the pump is calculated by the discrete phase model. Then, the simu...
During the terminal mining phase of in gobs, the advancing rate slows gradually, and the distribution of spontaneous combustion three-zone in gobs undergoes significant changes, yet there remains a lack of simple and effective prediction methods. To...
Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often de...
Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consi...
Recent advancements in science and technology have resulted in products with enhanced reliability and extended lifespans across the aerospace and related sectors. Traditional statistical models struggle to assess their reliability accurately, prompti...
Laser powder bed fusion (LPBF) is widely used in metal additive manufacturing to create geometrically complex parts, where heat transfer and its affected temperature distribution significantly influence the parts’ materials’ microstructur...
The friction behavior in the tool-chip interface is an essential issue in aluminum matrix composite material (AMCM) turning operations. Compared with conventional cutting, the elliptical vibration (EVC) cutting AMCM has attractive advantages, such as...
In the process of production logging to evaluate fluid flow inside pipe, logging tools that force all flow to pass through a small measuring pipe are commonly utilized for measuring mixture density. For these logging tools, studying the fluid flow ph...
The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems...
This study presents an integrated approach combining bibliometric analysis and machine learning to explore research trends and predict the performance of cement pastes containing bio-based phase change materials. A bibliometric review of 5928 article...
Accurately predicting the thermal characteristics and heat transfer distribution of the rotating detonation engine (RDE) and acquiring a clear understanding of the performance and mechanism of the rotating detonation are of great significance for ach...
With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decompo...
As the carriers of massive data, data centers are constantly needed to process and calculate all kinds of information from various fields and have become an important infrastructure for the convenience of human life. Data centers are booming around t...
A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data,...
In this paper, model predictive control (MPC) is proposed for single-phase electric springs (ESs) with the help of the existing δ control, which is realized by controlling the instantaneous phase angle of the predefined sinusoidal reference of...
The examples of phase equilibria in binary systems, solid/liquid (SLE), liquid/liquid (LLE), vapor/liquid (VLE), as well as liquid/liquid equilibria in ternary systems mainly containing ionic liquids (ILs), or the infragrance materials, or pharmaceut...
We present a novel approach that combines the concept of reconstructed phase spaces with neural network time-series predictions. The presented methodology aims to reduce the parametrization problem of neural networks and improve autoregressive neural...
While traditional one-dimensional and three-dimensional numerical simulation techniques require a lot of tests and time, emerging Machine Learning (ML) methods can use fewer data to obtain more information to assist in engine development. Combustion...
Recently, the control of multiphase electric drives has been a hot research topic due to the advantages of multiphase machines, namely the reduced phase ratings, improved fault tolerance and lesser torque harmonics. Finite control set model predictiv...
Phase separation processes facilitate the formation of membrane-less organelles and involve interactions within structured domains and intrinsically disordered regions (IDRs) in protein sequences. The literature suggests that the involvement of prote...
Traditional techniques for detecting materials have been unable to coordinate with the advancement of material science today due to their low accuracy and high cost. Accordingly, machine learning (ML) improves prediction efficiency in material scienc...
In the development of future automotive systems, safety and performance are crucial considerations. The reliable operation of Drum-type Electromechanical Brakes (D-EMBs), key components responsible for vehicle braking, is essential. Previous research...
High-entropy alloys (HEAs) are a novel class of materials that exhibit exceptional mechanical, thermal and corrosion-resistant properties, making them highly promising for various industrial applications. The complex and variable compositions of HEAs...
In this study, a robust predictive power control (R-PPC) method for an N*3-phase permanent magnet synchronous motor (PMSM) is developed in the field of flywheel energy storage systems application, which can effectively improve robustness against indu...
Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of vol...
In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the...
The high-performance control technology of multi-phase motors is a key technology for the application of multi-phase motors in many fields, such as electric transportation. The model predictive current control (MPCC) strategy has been extended to mul...
Six-phase machines are increasingly used in safety-critical applications due to their inherent fault-tolerant capabilities. Due to the greater complexity of controlling six-phase machines and the fast dynamics required in safety-critical applications...
Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction...
The problem of control of stator currents in multi-phase induction machines has recently been tackled by direct digital model predictive control. Although these predictive controllers can directly incorporate constraints, most reported applications f...
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