Configuration Parameters Research Articles

An enhanced hybrid scheme for ship roll prediction using support vector regression and TVF-EMD

In order to represent the complex characteristics of ship roll motion such as nonlinearity, uncertainty, and time-varying dynamics thus improving the ship roll prediction accuracy, an enhanced hybrid prediction scheme is proposed by using improved swarm intelligent optimization, time series decomposition, and machine learning. Firstly, the novel time-varying filtering-based empirical mode decomposition (TVF-EMD) is employed to decompose the ship roll motion data into multiple mode components with dynamic time-varying characteristics, which immunes of the mode mixing and intermittency problems of traditional empirical mode decomposition (EMD). Then, the support vector regression is utilized to train and predict mode components. Finally, the predicted results of individual components are reconstructed to achieve the final ship roll prediction angles. To avoid the unfavorable influence of manual parameter selection for TVF-EMD, an improved black widow optimization algorithm is employed to optimize the parameter configuration. The feasibility and effectiveness of the enhanced hybrid model are validated by ship roll prediction simulation based on the measured data of M.V. YuKun at sea. The experimental results show that the enhanced hybrid scheme can improve the prediction accuracy of ship roll motion and outrank those by using methods of EMD, ensemble empirical mode decomposition, and variational mode decomposition.

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer.

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

Hydrothermal analysis of parallel and symmetric microchannels with phase change slurry and porous fin designs

The dynamic information of microchannel systems with parallel and symmetric configurations is insufficient, which limits our ability to compare and optimize these systems in all directions. In this study, thermal and hydraulic characteristics in parallel and symmetric microchannel heat sinks (MCHSs) with porous fins cooled by phase change slurry are investigated. A 3-D steady state conjugate model is adopted to compare the effects of fin configuration, coolant type and structural parameters on hydrothermal properties of wavy MCHS. The variations of dimensionless parameters involving Nu, f, η (temperature control effectiveness) and PEF (performance evaluation factor) are considered to demonstrate the effectiveness of combined design on overall performance enhancement of wavy MCHS. Results indicate that lower temperature increment and higher pressure loss appear in wavy configuration with porous fins than in straight mode, and lowest thermal resistance arises in parallel porous fin design. At the narrow position of symmetric channel, the fluid changes from the low-speed drop-shaped micelle to high-speed bullet-shaped one with increasing A, which leads to higher Nu with larger pressure drop penalty. Due to the extended flow route and improved fluid mixing, η increases with decreasing wavelength in all cases, and η of porous microchannels with both phase change slurry and parallel fins is much larger than that of symmetric one and the one with only base fluid. Compared with wavy fin configurations with small and large ε, lower temperature and its more uniform distribution happen in the mode with medium porosity. In comparison with basic configuration, the parallel configuration with slurry as cooling medium has the maximum PEF value when the pore size is 0.14 mm, up to 1.64. Larger f increase occurs in wavy channel with increasing N, and the space shortening between adjacent channel cavities in symmetric mode makes the adjoining Dean vortices easier to contact and repel, thus making it appear larger rise of f than parallel mode. Compared with other channel parameters, it is not effective to improve the overall performance of wavy MCHS with porous fins by adjusting α. Overall, the throat effect of symmetric MCHS significantly occurs in channel configuration with larger A, bigger λ, lower ε, smaller dp, more N, and lesser α, leading to higher f rise than the parallel mode.

Enhanced PET image reconstruction utilizing morphological filtering and MLEM algorithm

Positron Emission Tomography (PET) image reconstruction remains a pivotal area in PET technology, critically influencing clinical diagnostic outcomes. Addressing the need for enhanced image quality, this study introduces a novel algorithm for PET image reconstruction. This algorithm integrates a penalty mechanism, morphological filtering, and the Maximum Likelihood Expectation Maximization (MLEM) algorithm, aiming to improve reconstructed image quality significantly. The operational process of the algorithm within each iteration encompasses two primary phases. Initially, image reconstruction is accomplished via the MLEM algorithm, followed by the application of a morphological filter to attenuate noise in the reconstructed image. Simulation experiments demonstrate that this algorithm effectively mitigates noise while preserving crucial details, such as image edges. Notably, this method presents the dual benefits of straightforward parameter configuration and ease of implementation. The results indicate a substantial enhancement in noise suppression and fine structure preservation in the reconstructed images, marking a significant advancement in PET image reconstruction techniques.

Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

This study aims to utilize deep learning (DL) technology to address the issue of carbon soot emission in traditional internal combustion engines. Spraying and combustion experiments were conducted in a constant volume combustion (CVC) chamber using n-hexanol and graphene oxide nanoparticles as partial substitutes for diesel, with the goal of predicting their emission characteristics based on experimental data. Initially, spray and combustion data under different operating conditions were collected using the CVC experimental setup, and outliers were removed using the fast outlier detection (FOD) algorithm. Subsequently, the learning rate and the number of hidden layer neurons in the Bidirectional Recurrent Neural Network (BiRNN) model were optimized using the Sparrow Search Algorithm (SSA) to enhance the model's accuracy and generalization ability. Unlike previous research methods, the novel FOD-coupled SSA optimization method used in this study ensures the optimal parameter configuration of the speed prediction model while accelerating the convergence speed, which ultimately improves the stability of the model. In addition, the optimal output variables were determined based on the Spearman and Pearson correlation coefficient rules. Finally, the effectiveness of the predictive model was validated using evaluation metrics such as root mean square error (RMSE) and R-squared (R2). The results indicate that the KL coefficient for D80H20GO20 fuel in the CVC device is minimal. The FOD-SSA-BiRNN model achieved R2 values of 0.99409, 0.99529, and 0.99692 for D100, D80H20, and D80H20GO20 fuels, respectively. Therefore, this study provides an effective method for the online accurate prediction of carbon soot emissions in mixed fuels.

Unsteady MHD flow analysis of hybrid nanofluid over a shrinking surface with porous media and heat generation: Computational study on entropy generation and Bejan number

The use of hybrid nanofluids in real-world situations is essential for enhancing the efficiency of heat transmission, especially in cooling semiconductor technology and industrial processes. The entropy generation on unsteady Al2O3-Cu/H2O hybrid nanofluid flow over a three-dimensional shrinking sheet is addressed numerically in this study, taking into account the effects of magnetohydrodynamic (MHD), nonlinear thermal radiation, porous media, heat generation, viscous dissipation, joule heating, and convective conditions. Using well-known non-similarity transformations, the model is carefully converted from partial differential equations (PDEs) to ordinary differential equations (ODEs). Afterwards, the behaviors of critical physical characteristics are uncovered across different parameter configurations by a numerical solution using the finite difference technique in bvp4c MATLAB. The velocity profiles of hybrid nanofluid grow proportionately with increases in the values of ϕ2,M,A,K and λ parameters. As Rd,θw,Ec,Bi,H and ϕ2 increase, the temperature of the hybrid nanofluid rises, but as M and A increase, the temperature falls. The local skin friction in both the x and y- directions is increased by enhancing the unsteadiness A, nanoparticle volume fraction ϕ2, magnetic M, porous media K, and the ratio of strain rate λ parameter on the shrinking surface. The local Nusselt number is improved by cumulative the unsteadiness A, nanoparticle volume fraction ϕ2, magnetic M, and Biot number Bi parameter in the direction of a shrinking surface, while the Nusselt number decreases when Eckert number Ec, thermal radiation Rd, heat production H, and temperature ratio θw are improved. An upsurge in the magnetic parameter leads to the development of entropy generation. Increasing levels of the magnetic parameters led to a reduction in the Bejan number. At a nanoparticle volume fraction of 1 % and a nonlinearity radiation scenario, where the unsteadiness values are 0.5, the Nusselt number for hybrid nanofluid shows an estimated improvement of 10.55 % compared to regular nanofluid.

Effects of Different Grazing Treatments on the Root System of Stipa krylovii Steppe

Plants’ root properties are closely related to their ecological adaptability. This study aimed to clarify the differences in root properties of Stipa krylovii under different grazing disturbances. The morphological characteristics of root length, root surface area, root volume, root tip number, specific root length, and specific surface area of S. krylovii were compared under no grazing, light grazing, moderate grazing and heavy grazing conditions. The ecological adaptability to grazing pressure was also examined. Results showed that the underground biomass density decreased with the increase in grazing intensity. Grazing disturbance can lead to changes in plant community characteristics, and roots adapt to changes in these environmental factors by adjusting their distribution. Among the six root configuration parameters, those under light grazing were significantly higher than those under the other grazing types. The root length and root surface area were concentrated in the range of 0–2 mm. Mild grazing and moderate grazing were conducive to fine root penetration and contact with soil. Moderate grazing disturbance was beneficial to grassland vegetation productivity and played an important role in the stability and sustainable utilization of grassland ecosystem.

Thermohydraulic performance analysis and optimization of stripe patterned hybrid-wetting surface condensation by 3D steady simulation

The configuration parameters of stripe patterned hybrid-wetting surfaces (SPHWSs) had a significant effect on the thermohydraulic performance of condensation. By using Taguchi-GRA method modified by entropy weight for optimization, Nusselt number (Nu) and thermal performance factor (TPF) reached the highest increment of 225.9% and 252.8% at vapor mass fraction (ω) equal to 15%, respectively, and the maximum reduction of Fanning friction coefficient (f) was −41.7% at ω = 30%, as compared without SPHWSs. A 3D steady CFD-aided method based on pseudo transient under-relaxation model was developed to predict Nu, f, and TPF of SPHWSs for different configuration parameters, including oblique pattern angle (β), hydrophilic (Wi) and hydrophobic (Wo) pattern width, hydrophilic (θi), and hydrophobic (θo) contact angle. The result indicated that the simulation was in good agreement with the experimental data. θi had a more pronounced effect on the thermohydraulic performance compared to other factors. Notably, the droplet reached a specific dynamic force balance at θi = 60°, effectively restraining near-wall erratic flow and enabling cost-optimal heat transfer.

Omni-polarized Faraday isolator based on non-Hermitian Faraday system

Non-Hermitian systems have recently attracted significant attention in photonics due to the realization that the interplay between gain and loss can lead to entirely new and unexpected features. Here, we propose and demonstrate a non-Hermitian Faraday system capable of non-reciprocal omni-polarizer action at the exceptional point. Notably, both forward and backward propagating light with arbitrary polarization converge to the same polarization state. Leveraging the robustness and non-reciprocity of the non-Hermitian Faraday system, we realize an omni-polarized Faraday isolator that can effectively isolate any polarized light without the need for a polarizer at the incident port of backward propagation. Remarkably, under the given parameter configuration, the isolator achieves a maximum isolation ratio of approximately 100 dB and a minimum isolation ratio of around 45 dB for various polarized light, accompanied by near-zero insertion loss. Furthermore, our research reveals the remarkable tolerance of the non-Hermitian Faraday isolator to nonlinear effects. This unique characteristic allows us to harness nonlinear effects to achieve various optical functions, all while maintaining excellent isolation performance. The proposed non-Hermitian Faraday system paves the way for the realization of magnetically or optically switchable non-reciprocal devices.

A Method for micro-vibration test analysis of small satellites with laser loading

Based on the parameters of the structure configuration and equipment layout of a small laser satellite in the design stage, the in-orbit micro-vibration simulation analysis of the satellite is carried out by finite element method, which mainly includes [1-2]: 1) modal analysis obtaining the main vibration frequency and modal shape of the satellite in orbit; 2) micro-vibration analysis of the satellite in orbit with the micro-vibration disturbance of the moment gyroscope as the excitation condition. Through the in-orbit micro-vibration simulation analysis of the satellite, we can obtain the vibration transmission relationship from the moment gyroscope to the laser communication load and the micro-vibration state of the laser communication load.

Calibration requirement for Epoch of Reionization 21-cm signal observation. Part III. Bias and variance in uGMRT ELAIS-N1 field power spectrum

Power spectrum of H i 21-cm radiation is one of the promising probes to study large scale structure of the universe and understand galaxy formation and evolution. The presence of foregrounds, that are orders of magnitude larger in the same frequency range of the redshifted 21-cm signal has been one of the largest observational challenges. The foreground contamination also hinders the calibration procedures and introduces residual calibration errors in the interferometric data. It has been shown that the calibration errors can introduce bias in the 21-cm power spectrum estimates and introduce additional systematics. In this work, we assess the efficacy of 21-cm power spectrum estimation for the uGMRT Band-3 observations of the ELAIS-N1 field. We first evaluate the statistics of the residual gain errors and perform additional flagging based on these statistics. We then use an analytical method to estimate the bias and variance in the power spectrum. We found that (a) the additional flagging based on calibration accuracy help reduce the bias and systematics in the power spectrum, (b) the majority of the systematics at the lower angular scales, ℓ< 6000, are due to the residual gain errors, (c) for the uGMRT baseline configuration and system parameters, the standard deviation is always higher than the bias in the power spectrum estimates. Based on our analysis we observe that for an angular multipole of ℓ∼3000, 2000 hours of `on source time' is required with the uGMRT to detect redshifted 21-cm signal at 3-σ significance from a redshift of 2.55. In this work we only consider the power spectrum measurement in the plane of the sky, an assessment of residual gain statistics and its effect on multifrequency angular power spectrum estimation for the uGMRT and the SKA like telescopes will be presented in a companion paper.

Dynamic modeling of shaping-changing cross parachute

The research explored the dynamic simulation of a cross parachute as an actuator, and proposed a method for dynamic modeling to adjust the configuration of the cross parachute. Initially, based on the method of multi-body dynamics modeling, a coordinate system necessary for system simulation was established. Subsequently, by incorporating the changing configuration parameters as input, a dynamic simulation model of the cross parachute with adjustable configuration was developed. Dynamic simulations were executed for the suggested model under two scenarios: changing configuration and symmetric configuration. The simulation results indicated that the parachute with changing configuration could generate radial forces related to the inflow velocity, with the force significantly increasing as the inflow velocity increased. The modeling approach offers a new perspective for the study of the dynamics of using a cross parachute as a control actuator, while the simulation results provide a degree of reference for research into the control force offered by a variable configuration cross parachute.

Long Short-Term Memory Parameter Optimization Based on Improved Sparrow Search Algorithm for Molten Iron Quality Prediction

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

StructDiffusion: End-to-end intelligent shear wall structure layout generation and analysis using diffusion model

Shear wall structures are widely used in high-rise buildings. However, the design process of shear wall structures suffers from inefficiencies. This paper introduces StructDiffusion, an end-to-end intelligent system for shear wall layout generation and analysis. The proposed approach tackles the layout design task by employing a novel diffusion model architecture to generate conditional images. Key components of the proposed method include pretrained diffusion models, ControlNet for conditional control, and LoRA for efficient adaptation. Notably, the method allows for architectural plan images and basic textual design conditions as inputs, enabling manipulation of the generated layouts by adjusting attributes such as building height and seismic intensity. To evaluate the quality of the model's design swiftly, this paper presents a comprehensive evaluation framework that incorporates perceptual and structural validity metrics. Through experimental analyses, this paper demonstrates the effectiveness of the proposed model in generating layouts, surpassing the capabilities of GAN-based methods. Furthermore, our study investigates model-specific parameter configuration, text-guided capabilities, code compliance, and computational efficiency. StructDiffusion significantly enhances the automation and intelligence of structural engineering workflows. The framework effectively addresses challenges associated with instability, data scarcity, and limitations in assessment. This pioneering application of diffusion models opens up promising avenues for advancing data-driven structural design.

Machine learning-based characterization of friction stir welding in aluminum alloys

This research presents a machine learning (ML) approach that integrates Bayesian theorem to predict residual stress, plastic deformation, and peak temperature in friction stir welding (FSW) of different types of aluminum alloys. The training data was acquired from extensive numerical simulations of FSW conducted across various input parameter configurations. The regression analysis exhibits strong predictive performance for residual stress, peak temperature, and plastic deformation, achieving determination coefficients of 0.969, 0.955, and 0.919, respectively. The high prediction performance of the developed model is primarily attributed to Bayesian optimization, distinguishing it from other conventional ML methods. The results further demonstrate the efficacy of the proposed model in assessing the relevance of input features to prediction efficiency and output targets. For instance, concerning processing parameters, the predictive performance is closely correlated with the significance of preheating, traverse speed, and rotational speed in FSW, compared to welding force and plate thickness. On the other hand, considering material properties, it was observed that the hardness value of aluminum alloys emerged as a crucial predictor in the model so that alloys with moderate hardness values showed greater welding consistency and significantly enhanced prediction accuracy.

Multi-scale collaborative prediction of optimal configuration for carbon fiber woven composites based on deep learning neural networks

Since carbon fiber woven composite panels contain complex fiber bundle structures, the aim of this paper is to investigate the factors influencing the macro-equivalent properties and topological flexibility of composite panels resulting from variations in fiber bundle configuration. Firstly, the structural topology optimization design of carbon fiber composite plates was achieved using a multi-scale modeling approach with the variable density method. Nonlinear relationships between carbon fiber bundle configuration parameters and macroscopic equivalent properties, as well as macroscale topological flexibility, were determined using deep learning methods, respectively. The maximum error in the predicted values of the deep learning network model does not exceed 0.5% when compared with the results of the finite element calculations. In addition, utilizing deep learning networks to predict macro-equivalent properties of composites can significantly decrease computational time. The impact of varying the number of filaments in fiber bundles and the spacing of the fiber bundles on the flexibility of the topology was determined using a deep learning network. The optimal topological flexibility, along with its corresponding fiber bundle spacing and number of fiber bundle filaments, are globally searched within the design domain using an adaptive genetic algorithm. The study in this paper can provide a reference for designing fiber bundle configurations of composites for practical applications.

Optimally Splitting Solar Spectrums by Concentrating Solar Spectrums Splitter for Hydrogen Production via Solid Oxide Electrolysis Cell

The concentrating solar spectrums splitter (CSSS)-driven solid oxide electrolysis cell (SOEC) is an attractive technology for green hydrogen production. The CSSS mainly comprises a concentrating photovoltaic (CPV), which converts sunlight with shorter wavelengths into electricity, and a concentrating solar collector (CSC), which converts the remaining sunlight into heat. However, the optimal splitting of the solar spectrums is a critical challenge that directly impacts the efficiency and normal operation of the SOEC. To address this challenge, a mathematical model integrating the CSSS with the SOEC is developed based on principles from thermodynamics and electrochemistry. By analyzing the requirements of electricity and heat for the SOEC, the model determines the optimal configuration and operational parameters. The results show that the anode-supported type, higher operating temperature, larger inlet flow rate of water, higher operating pressure of the SOEC, higher operating temperature of the CSC, and larger electric current of the CPV contribute to allocating more solar spectrums to the CSC for heat generation. However, the greater effectiveness of the heat exchangers, higher operating temperature, and larger optical concentration ratio of the CPV exhibit contrasting effects on the spectrum allocation. The obtained results provide valuable theoretical guidance for designing and running the CSSS for hydrogen production through SOEC.

Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine

Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.

Wavelet technique and FEA for modal identification in damaged URM shear walls

In existing masonry buildings, the structural capacity with respect to horizontal actions is due to the in-plane response of shear walls, usually evaluated in terms of effective stiffness and drift capacity. The objective of this work is to provide first results to the damage sensitivity in diagonal cracking of URM shear walls and a study methodology based on vibration tests to assess the structural health of existing masonry buildings. The state of pre-existing discontinuities caused by previous damage events or material degradation may play a role in the in-plane response of shear walls. For this, an experimental campaign was carried out on three typologies of UnReinforced Masonry (URM) walls, simulating different damage situations, subjected to Shear-Compression (SC) and vibration tests in order to correlate the structural behaviour with changes in modal parameters. From the vibrational response acquired during the SC test, the modal parameters related to the pre-damage and post-damage condition were identified using the Continuous Wavelet Transform identification technique. The experimental results are then used to implement a Finite Element Model (FEM) with a 2D homogenization approach to simulate the behaviour of URM walls. A parametric study through FEM analysis is carried out in order to investigate the change in modal parameters for different damage configurations and for triggering strut mechanisms.

Integrating Patient-Based Real-Time Quality Control (PBRTQC) in a New Field: Inter-Comparison between Biochemical Instrumentations with LDL-C.

Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC's efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories. From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi'an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients' data. Moreover 40 patients' LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%. We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L-6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy. The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field.