Experimental Comparison Research Articles

A class-center fine-tuning prototypical network for few-shot fault diagnosis of turnout switch machine driven by multi-source signals

The turnout switch machine is mainly responsible for controlling the switch converting and locking, which is a crucial component of the rail transit signal system, and its reliability directly impacts the safety of rail transit transportation. However, many existing methods for diagnosing faults in turnout switch machines suffer from low accuracy and poor generalization capabilities. The primary issue is that the fault data of turnout switch machines are scarce, and the single-source data cannot fully reflect the fault characteristics. To address these issues, this paper introduces a fault diagnosis approach for the turnout switch machine based on a class-center fine-tuning prototype network (CFPN). Firstly, current signals of three phases are fused with different weights through an adaptive data fusion strategy, using the fused signal as input data. Secondly, a multiple weighted feature fusion network (MWFFN) based on the activation function Meta-ACON that can learn whether to activate neurons is proposed, which effectively extract and fuse features at different levels in the fusion signal. In addition, a fine-tuning center balance strategy is proposed to effectively expand the distance between prototypes and enhance intra-class aggregation. Finally, a few-shot sample experiment is conducted to verify the performance of CFPN, using fused current data collected from’ a switch machine. In the comparative experiment, CFPN shows good fault diagnosis performance, the fault diagnosis accuracy reaches up to 99.08%. At the same time, fused vibration data are used to conduct a cross-domain experiment on CFPN, and the results indicate that CFPN still shows good classification effect and has good generalization performance, demonstrating its high practical application value.

IPSRM: An intent perceived sequential recommendation model

Objectives:Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process. Methods:This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process. Results:Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions. Conclusions:Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.

A facile approach for fabricating g-C3N4-based materials as metal-free photocatalysts

This study presents a novel, straightforward approach for synthesizing graphitic carbon nitrides (g-C3N4, g-CN) from melamine, requiring merely 30 minutes of thermal holding at temperatures ranging from 400 to 550 °C in an atmosphere comprising either nitrogen or air. Elemental analysis, X-ray powder diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), diffuse reflectance (UV-Vis DRS), photoluminescence (PL) spectroscopy and scanning electron microscopy (SEM) were employed to assess the quality of the as-prepared powders. Furthermore, theoretical calculations utilizing the Density Functional Theory (DFT) method were conducted to reinforce the experimental findings of the research. A further investigation of the thermal stability of the selected sample was conducted using a unique combination of thermogravimetry–coupled with differential scanning calorimetry, quadrupole mass spectrometry (TG-DSC-MS) and advanced temperature-programmed desorption (TPD) analyzes. The current study focuses on the effect of synthesis conditions (temperature and nitrogen/air environment) on the structure, morphology, and photocatalytic performance of g-C3N4 compounds synthesized using this approach. The g-C3N4-based materials were examined as potential photocatalysts using the acid orange 7 (AO7) photodegradation methodology. To enable comparison of the photodegradation experiments, two separate lamps with wavelengths of 360 nm (UV light) and 420 nm (VIS light) were utilized. The primary objective was to present a novel method for the synthesis of g-C3N4-based materials. This was achieved by demonstrating that organic composites generated at lower temperatures have the best photocatalytic capabilities. Furthermore, the approach to achieving high-quality photocatalysts was shown to be cost-effective, environmentally friendly, and scalable.

Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin

Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin

Research on the influence of multi-agent deep deterministic policy gradient algorithm key parameters in typical scenarios

Abstract The MADDPG algorithm is widely used and relatively complete, but there is no intuitive data support for the values of some key parameters. Therefore, the influence law of key parameters on MADDPG in typical scenarios has been studied in this paper. Firstly, three typical experimental scenarios were identified, including collaborative cooperation, collaborative opposition, and collaborative pursuit with basic parameters and hyperparameters. Then, a research plan was formulated by using the control variable method to study the influence of learning rate, reward discount coefficient, and reward function coefficient on algorithm performance. Based on a large number of experimental data comparisons, the optimal values of each parameter under three experimental scenarios were obtained. The results showed that the optimal reward discount coefficient for all three scenarios was the same, indicating that its impact on scene complexity was relatively small. For the optimal learning rate, there was a general trend that the lower complexity collaborative cooperation scenario had an optimal value lower than that of the higher complexity collaborative pursuit and collaborative opposition scenarios. As for the reward coefficient, it could be concluded that when the reward coefficient was large in collaborative cooperation and collaborative opposition scenarios, the convergence and speed of the reward curve became poorer. The reward coefficient in the collaborative pursuit scenario had a less significant impact on the performance of the algorithm.

Partial abrasion modeling and variable load characteristics analysis for high-speed load sensing electro-hydrostatic actuator pump of aircraft

The load-sensing electro-hydrostatic actuator (LS-EHA) allows to reduce the thermal output and enhance the dynamic characteristics of the more electric aircraft (MEA). In addition, the forecasting of the LS-EHA is crucial for its efficient functioning. This study proposes a novel model for the analysis of overturning and wear characterization of the slipper, while focusing on the partial abrasion. The transient thermoelastic hydrodynamic (TEHD) lubricating analysis of the slipper pair is solved by an analytical model along with the finite difference method. In the proposed partial abrasion model, the non-uniformity of the oil film thickness and the partial abrasion contour for the slipper bottom surface are considered through the discrete friction area approach in order to reach a higher precision. Afterward, the overturning behavior characteristics of the slipper and the variation law in its overturning behavior under different rotation speeds, load delivery pressures, and swashplate angles are analyzed. The obtained results demonstrate that the lubricating oil film field is unevenly distributed, and the overturning behavior of the slipper has certain variable load characteristics. Finally, the effectiveness and precision of the proposed partial abrasion model are validated through comparative simulation experiments and analysis.

Analysis of difficult flotation mechanisms for coarse low-rank coal: Comparing fluidized-bed and mechanical flotation technologies

Efficient flotation of coarse low-rank coal is essential for enhancing energy utilization in the mineral processing industry. As the beneficiation equipment scales up, the precision of particle size classification before flotation decreases, leading to a higher coarse particle content in the feed, which poses significant challenges to traditional flotation processes. Recent advancements in fluidized bed flotation technology have demonstrated considerable advantages in the flotation of coarse-grained metallic minerals; however, its application to coarse low-rank coal remains relatively unexplored. This study investigates the potential of fluidized bed flotation technology for coarse low-rank coal, employing various analytical methods to elucidate difficult flotation mechanisms. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were utilized to comprehensively characterize the surface morphology and chemical composition of coal samples. The influence of increasing particle size on flotation performance was systematically assessed through measurements of induction time and critical detachment amplitude. Comparative flotation experiments were conducted to evaluate the efficacy of traditional mechanical flotation against fluidized bed flotation technologies. Results indicate that the presence of cracks and oxygen-containing functional groups on the coal surface significantly enhances hydrophilicity, hindering flotation recovery rates. With increasing particle size, the induction time between particles and bubbles lengthens, while the critical detachment amplitude decreases, weakening the attachment and increasing detachment likelihood. Although high-efficiency collectors enhance coal particle hydrophobicity, they cannot fully mitigate the turbulence disrupting particle-bubble interactions in mechanical flotation. Conversely, fluidized bed flotation effectively minimizes turbulence interference, leading to an approximate 40 % increase in the recovery rate of coarse low-rank coal (0.25–1.00 mm) compared to traditional mechanical flotation. This study presents a novel technological approach for the efficient recovery of coarse low-rank coal, contributing to the clean utilization of low-rank coal resources.

Compositional optimization for enhanced oxidation resistance of high-entropy carbide ceramics

Excellent oxidation resistance is essential for the viability of high-entropy carbides (HECs) in high-temperature environments. However, the vast HEC compositional space presents a significant challenge in developing desirable formulations with improved oxidation tolerance. In this work, we establish a computational framework to guide the optimization of oxidation-tolerant HEC compositions and further validate the theoretical predictions through systematic oxidation experiments. Leveraging first-principles calculations, we initially determine the heterogeneous oxygen adsorption ability of different constituent elements in HECs, which is subsequently harnessed to regulate preferential oxidation and suppress the formation of detrimental oxides during the oxidation process. Incorporating this fundamental-level understanding, multi-objective optimization (MOO) was performed to identify a compositional region with potentially intrinsic oxidation immunity, achieving a balance between the compactness of the oxidation product and the thermodynamic stability of the HEC matrix. Based on our analysis, we propose a compositional design strategy involving the strategic reduction of elements with higher oxygen adsorption energies, such as Nb, Ta, and Ti in (NbTaZrHfTi)C, to effectively improve the oxidation performance of HECs. The theoretical findings are robustly corroborated by our comparative oxidation experiments, which demonstrate that the selected NbTa-depletion HEC exhibits superior antioxidant performance compared to the equiatomic counterpart. The integrated computational-experimental approach presented in this work serves as a powerful framework to accelerate the discovery and development of oxidation-resistant HECs, paving the way for their expanded applications in demanding high-temperature, oxygen-rich environments.

Experimental Comparison of Anomalous Reflectors Implemented With Local and Nonlocal Design Approaches

Experimental Comparison of Anomalous Reflectors Implemented With Local and Nonlocal Design Approaches

Experimental study of thermoconcentration convection in air–water and air–undecane mixtures

A quantitative experimental comparison was conducted between thermal convection in dry air and thermoconcentration convection in two gas mixtures: air–water vapor and air–undecane vapor, within the temperature range of 0–80 °C at normal atmospheric pressure. Convection in these mixtures is driven by temperature and concentration gradients of water (or undecane) vapor in the air. The stationary thermoconcentration convection is accompanied by continuous phase transitions of the fluids. The quantitative results of the experiments are represented in terms of the Nusselt number Nu and the effective Rayleigh number RaE, which is the sum of the thermal RaT and concentration RaC Rayleigh numbers. Quantitative laboratory measurements were performed using the thermocouple method and were supplemented with qualitative data from visual monitoring of transparent fluid flows using holographic interferometry. The cubic and quadratic temperature dependencies of RaCRaT−1 were determined experimentally for moist air and for the air–undecane vapor mixture, respectively. The significant role of moisture phase transitions in air convection is established. Neglecting these effects at 25 °C and using the ordinary RaT instead of actual RaE would result in an error exceeding 30%. At 38 °C, this error would increase to nearly 100%. At around 80 °C, thermoconcentration convection becomes almost entirely concentration-driven, as the high molecular disordered thermal motion is suppressed by the ordered convective motion generated by evaporation and condensation of water on the opposite heat exchangers of the convective cell.

Experimental comparison of First-law analysis of peak hour operation of GSHP system under optimized conditions

The present study presents a novel procedure to study energy – efficient performance of a Ground − Source Heat Pump (GSHP) system during peak hour space cooling and heating operations by integrating an experimental study with First law analysis and optimization techniques. The main objective of the study is to identify how closely the results of First law analysis agree with the actual experimental results when operated under optimized conditions. The experimental data obtained during peak hour operation of a 17.58 kW GSHP system for each three days of operation in summer and winter seasons are considered for the analysis. The Coefficient of Performance (COP) and Power Consumption (W) of the GSHP system are computed using an energy balance based on First law using the experimentally measured heat rejection by the Borehole Heat Exchangers (BHEs) and the cooling effect. For optimization using the Taguchi method, six distinct measured parameters, each at three different levels, are selected as variables. Analysis of Variance (ANOVA) was carried out on COP and W to ascertain the parameters that exert the most significant influence on the performance of the GSHP system. During peak hour operation, the temperature difference between the inlet and outlet water in the BHE was found to be 10–11 °C in cooling mode and 3 °C to 4 °C in heating mode operations. Temperatures of water in the BHEs and air at the heat pump, which contribute to the heat interaction, are found to be the most influential parameters for cooling and heating mode operations. The COP for cooling mode computed by the optimization technique, 3.59, closely agrees with the experimental COP of 3.73. Similarly, the optimized COP for heating mode, 3.84 is found to be closer to the experimental COP of 4.32. The optimized power consumption deviates by 14.21 % and 11.94 % from the experimental results for cooling and heating modes, respectively.

Active phase and amplitude temperature control method for cryostat: A case in the thermodynamic temperature international comparison system

Achieving highly stable temperature control is essential for cryostats. However, the cryocooler used in cryostats, such as the pulse tube cryocooler, produces regular sinusoidal temperature fluctuations due to its inherent working principles. To establish a highly stable temperature environment, an active phase and amplitude control method for suppressing temperature fluctuation is proposed in this paper, by which the optimal phase and amplitude can be predicted in theory. To validate this approach, a thermal response model was developed based on experiments conducted in the thermodynamic temperature international comparison cryostat at approximately 3.7 K. Based on this model, we conducted both single-stage and multi-stage active temperature control experiments. The results demonstrate that the temperature control effect is the best when the active temperature control current is added at the second cold head, the fluctuation of the comparison copper block from 3.3mK to 0.166mK with 94.9 % suppression rate. The multi-stage joint temperature control reduced the temperature fluctuation with an attenuation rate of 95.4 %. These findings offer significant potential for improving temperature stability in future thermodynamic temperature comparison experiments.

Integrated metabolomics and network pharmacology reveal the procoagulant mechanisms of Cirsium setosum extracts

As a medicinal plant, Cirsium setosum has excellent procoagulant effects and has long been used as a cure for hemoptysis, epistaxis, uremia and metrorrhagia caused by blood heat. However, the key medicinal part of C. setosum, as well as the biologically active substances that play a major role, are not known. In this study, the aboveground, underground and whole grass portions of C. setosum were subjected to a coagulation comparison experiment to determine the primary active procoagulant compounds. The main active procoagulant compounds of C. setosum were then screened using a comparative metabolomics analysis between aboveground and underground. Network pharmacology analysis was used to construct the “active ingredient-disease target-pathway” network. Finally, molecular docking was used to verify the binding ability and affinity between the key active ingredients obtained from the screening and the targets. The results indicated that the aboveground part of C. setosum could significantly shorten activated partial thromboplastin time (APTT) and that this part exerts substantial procoagulant effects. The total phenol, total flavonoid and total alkaloid content of the aboveground part was measured to be higher than those of the underground part and whole grass. Furthermore, comparative metabolomics analysis as well as the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database and literature search screening yielded 10 active substances, including naringenin, guanine, 2,4-di-tert-butylphenol, calycosin-7-O-beta-D-glucoside, flavone, vitexin, and tiliroside, which may be related to the coagulation-promoting properties of C. setosum and its therapeutic effects on coagulation-related disorders. Network pharmacological analysis revealed that C. setosum may exert procoagulant effects mainly through tiliroside, calycosin-7-O-beta-D-glucoside, and flavone, which act on key target proteins, such as SRC, PRKACA, EGFR, AKT1, MAPK3, and GSK3B. In summary, C. setosum exerts its procoagulant and therapeutic effects on coagulation-related diseases through multiple compounds, targets, and pathways.

Spectral-Galerkin methods for the fully nonlinear Monge-Ampère equation

In this paper, we develop two numerical methods, the Legendre-Galerkin method and the generalized Log orthogonal functions Galerkin method for numerically solving the fully nonlinear Monge-Ampère equation. Both methods are constructed based on the vanishing moment approach. To address both solution stability and computational efficiency, we propose a multiple-level framework for resolving discretization schemes. The mathematical justifications of the new approaches and the error estimates for the Legendre-Galerkin method are established. Numerical experiments validate the accuracy of our methods, and a comparative experiment demonstrates the advantage of Log orthogonal functions for problems with corner singularities. The results highlight that our methods have high-order accuracy and small computational cost.

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

FusionNGFPE: An Image Fusion Approach Driven by Non-global Fuzzy Pre-enhancement Framework

The majority of prevailing image fusion methods employ a global strategy, often resulting in a reduction of contrast. This study addresses this issue by proposing a novel image fusion approach called FusionNGFPE, specifically designed for the structural characteristics of infrared (IR) imagery. The approach introduces a contrast equalization algorithm based on the Fourth-order Partial Differential Equation (FPDE) to enhance background regions effectively. Considering the inherent differences between IR and visible (VIS) images, we developed a hybrid fusion strategy that combines the Expectation Maximization (EM) algorithm and Principal Component Analysis (PCA). Comparative analysis with state-of-the-art fusion methods shows that our proposed algorithm achieves superior performance in both qualitative and quantitative evaluations. To further demonstrate the practical significance of FusionNGFPE, we integrated this fusion framework into the RGBT target tracking task using the VOT-RGBT and OTCBVS datasets. Extensive comparative experiments confirm that the FusionNGFPE framework integrates seamlessly with the tracking task, significantly improving tracking accuracy across diverse scenarios.

A transient network model for cross-regional layered fire smoke diffusion in high-rise buildings

At present, research on smoke diffusion in high-rise building fires mainly focuses on the single smoke layer propagation within a limited area, with less attention paid to the smoke propagation under the joint action of the fire floor and the vertical shaft area. Moreover, research on fire spread often focuses on the composition of combustible materials and building structures, neglecting natural ventilation. To address the above issues, we propose a transient network model for layered smoke diffusion (CLDTN) based on multi-region integration and complex control parameters. This model considers the smoke stratification mode of the fire floor affected by the fire source and ventilation, as well as the vertical stratification mode of the shaft affected by the stack effect, achieving cross-regional smoke stratification and diffusion modeling through smoke coupling. Then, a time-dependent transient network model is used to analyze furniture materials and open ventilation conditions of doors and windows, and a differential equation for energy conservation in multiple scene layers is constructed to analyze the dynamic behavior of smoke propagation.The comparative experiment of smoke propagation between CLDTN and the Fire Dynamics Simulator (FDS) shows that CLDTN is more efficient in computation time and the model is more accurate.

Field trial of stable radio frequency transfer system in 200 km metropolitan optical fiber link.

In this paper, we demonstrate stable radio frequency (RF) transfer via metropolitan optical fiber link in the Beijing area. The phase variation of the RF signal is compensated by a phase conjugation method incorporating two high-performance phase-locked loops. The wavelength conversion module extends the transmission length to 200 km with only two parallel 50 km dark optical fibers available. We optimize the configuration of dispersion compensation and optical amplification due to the high loss (0.31 dB/km) of the optical fiber link. At the same time, comparative experiments verify the short-term instability limitation that arises from the group velocity dispersion of the optical fiber link. The measured standard Allan deviation of the 2.4GHz RF transmission system with dispersion compensation is 4.5 × 10-14/1 and 2.6 × 10-17/20 000s, which is superior to that of the reference rubidium clock. The short-term instability of the system is deteriorated to 2.5 × 10-13/1s without dispersion compensation.

Reinforcement Learning-Based Multimodal Model for the Stock Investment Portfolio Management Task

Machine learning has been applied by more and more scholars in the field of quantitative investment, but traditional machine learning methods cannot provide high returns and strong stability at the same time. In this paper, a multimodal model based on reinforcement learning (RL) is constructed for the stock investment portfolio management task. Most of the previous methods based on RL have chosen the value-based RL methods. Policy gradient-based RL methods have been proven to be superior to value-based RL methods by a growing number of research. Commonly used policy gradient-based reinforcement learning methods are DDPG, TD3, SAC, and PPO. We conducted comparative experiments to select the most suitable method for the dataset in this paper. The final choice was DDPG. Furthermore, there will rarely be a way to refine the raw data before training the agent. The stock market has a large amount of data, and the data are complex. If the raw stock market data are fed directly to the agent, the agent cannot learn the information in the data efficiently and quickly. We use state representation learning (SRL) to process the raw stock data and then feed the processed data to the agent. It is not enough to train the agent using only stock data; we also added comment text data and image data. The comment text data comes from investors’ comments on stock bars. Image data are derived from pictures that can represent the overall direction of the market. We conducted experiments on three datasets and compared our proposed model with 11 other methods. We set up three evaluation indicators in the paper. Taken together, our proposed model works best.

Fluorinated B-72 hybrid silver doped modified nanoparticle as a coating for biological corrosion protection of ancient bricks

In this paper, a superhydrophobic organic-inorganic composite coating containing fluorinated B72 hybrid methyl-modified silica/(Ag doped TiO2) by hexamethyldisilazane (HMDS) (H-SiO2/Ag/TiO2) was prepared by means of a mild and an environmental friendly method. Transparency, adhesion, hardness tests and photocatalytic performance were measured to determine the content of Ag in TiO2 which the TiO2/AgNO3 ratio is 0.008 g/mL. The synthesized Ag/TiO2 nanoparticles were characterized by X-ray diffraction (XRD), Raman spectra and transmission electron microscope (TEM). The highest contact angle of coated ancient bricks was 148.66° and the color change value of ΔE⁎ before and after coating was <5 which is within an acceptable range. The physical properties such as apparent porosity and water vapor permeability (ΔMp) were studied. After weathering from acid, alkali, salt, water and ultraviolet radiation, the bricks coated by the composite coatings still have good hydrophobicity that the contact angle were >120° and minor degree of surface corrosion comparing with the ancient bricks. The inhibitory effect of the composite coating on Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), and that on algae (Chlamydomonas and Dunaliella) were studied through bacteriostasis test and algae comparative experiments, respectively. Finally, the mechanism of the composite coating on antibacterial and anti-algae was studied. The results showed that the appearance of the ancient bricks was not changed after coating, and the anti-weathering, antibacterial and anti-algae properties of the ancient transformation were increased.