Adaptive Matching Research Articles

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

Adaptive Matching of Discrete Antenna Tuning Units of High Frequency Band to Varying Load Impedance

The issue of matching the antenna with thetransmitter-receiverplays a primary role in radio communications. The matching problem is relevant for different ranges and is solved in many ways. In the high frequency wave range, matching has a number of features when using an antenna coupler based on discrete elements consisting of capacitance and inductance boxes. The article discusses the concept of fast matching of high-frequency amplifying paths ofdecametric wavestransceivers with non-stationary loads using discrete antenna couplers. An innovative approach has been developed to the tuning process when changing the operating frequency and ensuring self-adaptation of the antenna coupler when changing the impedance, based on methods for quickly searching for the geometric proximity of points. A quick method for configuring the antenna coupler is proposed, eliminating the procedure for recalculating the input impedance based on the results of measuring the input impedance of the antenna coupler. When using it, the speed of setting the antenna coupler at the current operating frequency is limited only by the time of a single state change of the discrete power circuit, which in the case of electromagnetic relays is numerically equal to their switching time. The configuration of the antenna coupler using the method of fast search for the geometric proximity of points is based on a set of frequency cards with settings options. Such cards can be recalculated for the reference plane between the generator and the antenna coupler input for all combinations of discrete elements in accordance with the known matrix model of a discrete power circuit. This eliminates the complicated procedure of recalculating the input impedance of the antenna using complex numbers. The technical result of the proposed innovative method of antenna coupler configuringis simplification, tracking of systematic errors of the impedance meter, elimination of time-consuming calculations with complex numbers, expansion of the method functionality, reduction of the tuning duration and improvement its quality. The practical significance lies in the fact that the implementation of the proposed technical solutions in the high frequency range communications means can significantly (about 100 times) limit the duration of preparation for a radio communication session, eliminate the procedure for recalculating the antenna input impedance and reduce the resource requirements of switching elements.

FedDA: Resource-adaptive federated learning with dual-alignment aggregation optimization for heterogeneous edge devices

Federated learning (FL) is an emerging distributed learning paradigm that allows multiple clients to collaborate on training a global model without sharing their local data. However, in practical heterogeneous edge device scenarios, FL faces the challenges of system heterogeneity and data heterogeneity, which leads to unfair participation and degraded global model performance. In this paper, we introduce FedDA, a resource-adaptive FL framework, which adapts to the client’s computing resources by assigning heterogeneous models of different sizes. To improve the performance of heterogeneous model aggregation and adjust to non-independent and identically distributed (non-i.i.d.) data, we propose a dual-alignment aggregation optimization method, i.e., parameter feature space alignment and output space alignment. Specifically, FedDA exploits the permutation symmetry property of weight space to permutate the model parameter positions via an adaptive layer-wise matching method, thereby aligning models with significant deviations in parameter feature space. FedDA mitigates the imbalance in parameter quantity between smaller and larger models through parameter expansion. Additionally, FedDA maps client labels into a uniform embedding space through output space alignment, thus reducing model parameter deviations due to non-i.i.d. data without additional client-side computing overhead. We evaluate the performance of FedDA on benchmark datasets, including FashionMNIST, CIFAR10, CIFAR100 and AGNews. Experimental results demonstrate that FedDA achieves up to 8.71% improvement in model accuracy compared to baseline methods, highlighting its effectiveness in addressing the challenges of heterogeneity.

Visual guidance technology of flying cars based on multilevel markers and depth

Split-type flying car will play an important role in the future transportation. This paper adopts a guidance method that couples visual information and depth information, and improves the docking accuracy through the mutual cooperation of the drone and the vehicle. Firstly, a multilevel docking marker is designed to achieve adaptive target matching within different distances during the docking process. The marker has strong robustness and can adapt to complex scenes such as occlusion, strong light, and large angle tilting, providing the redundant corner points required for machine vision detection pose information accurately. Secondly, a three-dimensional pose estimation algorithm is proposed, which can introduce depth information to correct the homography matrix. The algorithm combines the advantages of strong robustness to multilevel marker detection and high accuracy of depth information, and can output millimeter-level precision pose information in different environments, different inclination angles, and different occlusions. Finally, a flying car model experiment was carried out, and the results showed that the guidance technology can obtain millimeter-level precise pose information during the entire process of long distance-near distance-completion of docking, thus realizing precise docking.

An Underwater Target Location Algorithm Combined with Schmidt Orthogonalization

Abstract In underwater target localization research, where challenges such as low signal-to-noise ratio and multiple interferences exist in conventional matched field processing, this paper presents a novel approach combining compressed sensing with Schmidt orthogonalization. Initially, an orthogonalization procedure is applied to the observation matrix, yielding a new matrix with reduced column coherence and maximal adherence to the Restricted Isometry Property (RIP) constraint. Subsequently, the Sparse Adaptive Matching Pursuit (SAMP) algorithm from compressed sensing is employed to reconstruct the signal, leading to precise underwater target localization. Simulation experiments substantiate that this combined approach outperforms the conventional matched field algorithm by yielding a higher signal-to-noise ratio for target acoustic source localization and markedly reducing the count of interference virtual sources. Consequently, the performance of localization for targets is substantially enhanced.

Adaptive matching strategies for 3D digital image correlation in strain measurement of an aerostat envelope

To address the limitations of two-dimensional digital image correlation (2D-DIC) in measuring strain on the aerostat envelope, the more precise 3D-DIC has been introduced to handle curved surfaces. However, the increased computational load of 3D-DIC requires more efficient correlation strategies. This paper evaluates three basic matching strategies and introduces two adaptive strategies to enhance the efficiency of 3D-DIC. The experimental results show that the adaptive composite matching (ACM) strategy automatically switches strategies based on deformation, improving the matching correlation. Meanwhile, the adaptive grouping matching (AGM) strategy dynamically adjusts image groups based on real-time data, optimizing the computational speed and enhancing measurement flexibility. These strategies provide crucial support for the application of 3D-DIC in the monitoring aerostat envelope strain, especially in managing significant or uneven deformations.

Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data.

Highly adaptive multi-modal image matching based on tuning-free filtering and enhanced sketch features

The generalizability and adaptiveness of multi-modal image matching (MMIM) techniques are hampered by the nonlinear radiometric differences that vary in a highly non-uniform manner across different modal combinations. A major challenge is the extensive filter tuning required by many filter-based matching methods to mitigate the impact of radiometric aberrations. Additionally, the low correct matching rate resulting from the non-repeatability and low similarity of local or detailed features presents another significant obstacle. In this paper, we introduce a novel MMIM framework, called Adaptive Matching with enhanced Edge Sketches (AMES), for reducing manual interference in filter parameter configuration and improving the correct matching rate of extracted feature points. Specifically, AMES trains a support vector regression model to adaptively predict the optimized filtering parameters for each test image using its internal statistical factors as input. We also propose a sketch feature enhancement method, based on fusing multi-scale moment maps through principal component analysis, for cross-modal adaptive feature point extraction. We generate matching results by applying feature descriptors within the Log-Polar window and performing outlier removal following a coarse-to-fine strategy. In an evaluation involving four datasets comprising 1055 image pairs with 26 different cross-modal combinations, the assessment of 10,599 manually measured checkpoints demonstrates that AMES surpasses the state-of-the-art in terms of success rate, correct matching rate, point coverage, average matching accuracy, and spatial distribution uniformity. Our source code and multi-modal image datasets are publicly available at https://dpcv.whu.edu.cn/zm1/gksjj.htm.

Variable foraging and flower probing behaviour of sunbird pollinators of the South African Pelargonium fulgidum

ABSTRACT Whether foraging sunbird pollinators hover or perch, the type of perch used, and the direction of flower probing are generally thought to be determined by matching shapes of decurved bird bills and curved flower tubes, as well as inflorescence architecture. However, few studies have objectively quantified aspects of sunbird foraging behaviour to test predictions based on adaptive trait matching. I investigated sunbird foraging in a population of Pelargonium fulgidum, a species with slightly upcurved flowers borne on upward-oriented peduncles, which grows along the west coast of South Africa. Long-range photographs revealed that Cinyris chalybeus, a sunbird species with a slightly decurved bill, hovers or perches during foraging, and uses both the inflorescence and alternative vegetation structures as perch. Contrary to previous suggestions, both male and female sunbirds probe flowers from either of two alternative directions (sternotribic versus nototribic) in more or less equal proportions, and frequently display flexibility in both foraging behaviour and flower probing direction. Predominant or complete sternotribic and nototribic flower probing were explained by hovering versus use of an inflorescence perch respectively, whereas use of an alternative perch resulted in similar proportions of each mode of flower probing. The number of flowers per visited inflorescence and number and proportion of probed flowers did not vary with foraging behaviour or flower probing direction. Although certain foraging behaviours resulted in predictable flower probing directions, variation in foraging behaviour and the presence of many alternative perches provided by vegetation surrounding P. fulgidum plants resulted in variability in flower probing direction that appears not to affect foraging efficiency. Relatively weak reciprocal curvature of flower tube and sunbird bill, resulting in easy access to nectar from multiple feeding directions, likely facilitates flexible foraging. This study shows that environmental context may facilitate more complex foraging behaviour than that predicted by adaptive trait matching.

The Effectiveness of Zirconia Crowns Versus Metal Crowns in Anterior Teeth: In vitro Study.

The selection between Zirconia crowns and metal crowns for anterior teeth restorations is pivotal in prosthodontics due to their distinct properties. However, a comprehensive investigation into their effectiveness in anterior tooth restorations is warranted. This in vitro study aimed to compare the effectiveness of Zirconia crowns and metal crowns in anterior teeth restorations. A total of thirty extracted human maxillary central incisors were prepared to receive crowns and randomly allocated into two groups: group A, zirconia crowns; and group B, metal crowns. The crowns were fabricated following standardized protocols and cemented onto the prepared teeth. Various tests, including fracture resistance, marginal adaptation, and color-matching assessment, were conducted on the samples. The mean fracture resistance of Zirconia crowns (Group A) was determined to be 320 N (SD ± 25), whereas it was 280 N (SD ± 30) for metal crowns (Group B). Marginal adaptation assessment revealed a mean gap width of 50 microns (SD ± 10) for Zirconia crowns and 70 microns (SD ± 15) for metal crowns. Evaluation of color matching showed a higher percentage of acceptable matches for Zirconia crowns compared to metal crowns. Zirconia crowns exhibited superior fracture resistance, marginal adaptation, and color matching in comparison to metal crowns for anterior teeth restorations. These findings advocate for Zirconia crowns as a more effective choice for restoring anterior teeth, offering both strength and esthetic appeal.

Three-dimensional internal multiple elimination in complex structures using Marchenko autofocusing theory

Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media. They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image. With the development of seismic exploration into deep and ultradeep events, especially those from complex targets in the western region of China, the internal multiple eliminations become increasingly challenging. Currently, three-dimensional (3D) seismic data are primarily used for oil and gas target recognition and drilling. Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling. In this study, we propose an internal multiples prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory. This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps. Firstly, simulating direct waves with a 3D macroscopic velocity model. Secondly, using direct waves and 3D full seismic acquisition records to obtain the upgoing and downgoing Green’s functions between the virtual source point and surface. Thirdly, constructing internal multiples of the relevant layers by upgoing and downgoing Green’s functions. Finally, utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples. Compared with the two-dimensional (2D) Marchenko algorithm, the performance of the 3D Marchenko algorithm for internal multiple predictions has been significantly enhanced, resulting in higher computational accuracy. Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data, thereby exhibiting important theoretical and industrial application value.

IGH Complementarity Determining Region-3-Cytomegalovirus Protein Chemical Complementarity Linked to Better Overall Survival Probabilities for Glioblastoma.

Cytomegalovirus (CMV) has long been thought to have an association with glioblastoma multiforme (GBM), although the exact role of CMV and any subsequent implications for treatment have yet to be fully understood. This study addressed whether IGH complementarity determining region-3 (CDR3)-CMV protein chemical complementarity, with IGH CDR3s representing both tumor resident and blood-sourced IGH recombinations, was associated with overall survival (OS) distinctions. IGH recombination sequencing reads were obtained from (a) the Clinical Proteomic Tumor Analysis Consortium, tumor RNAseq files; and (b) the cancer genome atlas, blood exome-derived files. The Adaptive Match web tool was used to calculate chemical complementarity scores (CSs) based on hydrophobic interactions, and those scores were used to group GBM cases and assess survival probabilities. We found a higher OS probability for cases whose hydrophobic IGH CDR3-CMV protein chemical complementarity scores (Hydro CSs) were in the upper 50th percentile for several CMV proteins, including UL99 and UL123, as well as for CSs based on known B cell epitopes representing these proteins. We also identified multiple immune signature genes, including CD79A and TNFRSF17, for which higher RNA expression was associated with higher Hydro CSs. Results were consistent with the idea that stronger immunoglobulin responses to CMV are associated with better OS probabilities for GBM.

An Adaptive Similar Scenario Matching Method for Predicting Aircraft Taxiing Time

Accurate prediction of taxiing time is important in ensuring efficient and safe operations on the airport surface. It helps improve ground operation efficiency, reduce fuel waste, and improve carbon emissions at the airport. In actual operations, taxiing time is influenced by various factors, including a large number of categorical features. However, few previous studies have focused on selecting such features. Additionally, traditional taxiing time prediction methods are often black-box models that only provide a single prediction result; they fail to provide effective practical references for controllers. Therefore, this paper analyses the features that affect taxiing time from different data types and forms a taxi feature set consisting of nine key features. We also propose a taxiing time prediction method based on adaptive scenario matching rules. This process classifies the scenarios into multiple typical historical scenario sets and adaptively matches the current target scenario to a typical scenario set based on quantified rules. Then, based on the matching results, a pre-trained model obtained from the corresponding scenario set is used to predict the taxiing time of an aircraft in the target scenario, aiming to mitigate the impact of data heterogeneity on prediction results. Experimental results show that compared to baseline methods, the mean absolute error and root mean square error of the proposed method decreased by 4.8% and 12.6%, respectively. This method significantly reduces the fluctuations in results caused by sample heterogeneity and enhances controllers’ acceptance of prediction results from the model. It can be used to further improve auxiliary decision making systems and enhance the precise control capabilities of airport surface operations.

Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment.

The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that beneficial and deleterious mutations can have comparable fitness impacts, i.e. the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast Saccharomyces cerevisiae by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we found no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.

Single-photon lidar depth imaging based on sparsity adaptive matching pursuit method

Photon counting lidar has the ability to detect weak echo photons. However, in imaging applications, a small number of echo signals obtained in a very short acquisition time cannot get an accurate image estimation through the photon arrival time distribution histogram. Therefore, high-precision and fast imaging of an object using a small amount of weak echo photon signals is urgent to be solved. In this work, a sparse adaptive matching pursuit (SAMP) algorithm is proposed to process the echo photons without prior information to obtain the effective photon interval, which improves the reconstruction quality and imaging speed. By constructing a non-coaxial scanning imaging system to detect the face model, it has been experimentally proven that the proposed algorithm can achieve depth and reflectance image estimation under the condition of low power and low acquisition time of 4µs per pixel on average, and the mean absolute error (MAE) value is reduced by about 10 times compared to conventional reconstruction algorithm.

High-Frequency and High-Current Transmission Techniques for Multiple Earth Electrical Characteristic Measurement Systems Based on Adaptive Impedance Matching through Phase Comparison.

With the increase in groundwater exploration, underground mineral resource exploration, and non-destructive investigation of cultural relics, high-resolution earth electrical characteristic measurement has emerged as a mainstream technique owing to its advantageous non-destructive detection capability. To enhance the transmission power of the high-frequency transmitter in high-resolution multiple earth electrical characteristic measurement systems (MECS), this study proposes a high-frequency, high-current transmission technique based on adaptive impedance matching and implemented through the integration of resonant capacitors, a controllable reactor, high-frequency transformers, and corresponding control circuits. A high-current precisely controllable reactor with a 94% inductance variation range was designed and combined with resonant capacitors to reduce circuit impedance. Additionally, high-frequency transformers were employed to further increase the transmission voltage. A prototype was developed and tested, demonstrating an increase in transmission current at frequencies between 10 and 120 kHz with a peak active power of 200 W. Under the same transmission voltage, compared to the transmission circuit without impedance matching, the transmission current increased to a maximum of 16.7 times (average of 10.8 times), whereas compared to the transmission circuit using only traditional impedance matching, the transmission current increased by a maximum of 10.0 times (average of 4.2 times), effectively improving the exploration resolution.

Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.

An adaptive detection model for IPv6 extension header threats based on deterministic decision automaton

The IPv6 extension header mechanism, a new feature of the IPv6 protocol, enhances flexibility and scalability but introduces numerous security threats like firewall evasion and covert channels. Existing threat detection methods face limitations in detection types, universality, and speed. Hence, an adaptive detection model for IPv6 extension header threats (ADM-DDA6) is proposed. Firstly, standard rule sets are designed for common IPv6 extension headers, successfully detecting 70 types of threats from THC-IPv6 and ExtHdr tools using only 20 rules. Secondly, by parsing IPv6 extension headers, matching rules, establishing transition relationships, and deciding packet threat status based on final states (Normal or Abnormal), complex threats like header disorder and header repetition can be detected. Finally, an adaptive rule matching method is introduced, which dynamically selects rule sets based on IPv6 extension header types, effectively reducing rule matching time. Experimental results show that under different threat magnitudes, ADM-DDA6 is 32% faster than Suricata v6.0.12 and 21.2% faster than Snort v3.1.61.0 in detection speed. Additionally, as the number of threats increases, on commodity hardware, ADM-DDA6 incurs only a 0.7% increase in CPU overhead with no significant memory consumption increase, maintains maximum throughput, and exhibits minor performance changes under low and moderate network load conditions.

Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.

Enhancing Educational Design Capabilities through Interactive Mobile and Adaptive Learning Platforms: An Empirical Study

With the continuous advancement of educational technology, interactive mobile and adaptive learning platforms are playing an increasingly important role in the field of education. This is particularly evident in the development of educational design skills among education students, as these platforms showcase their unique value. Educational design capability is a crucial skill for education students, directly related to the quality of designing and implementing future teaching activities. Traditional methods of education often fail to fully consider individual student differences, resulting in inadequate cultivation of personalized capabilities. This study aims to explore and achieve precise cultivation of educational design capabilities in education students through interactive, mobile, and adaptive learning platforms. This paper first reviews the current application of interactive mobile and adaptive learning platforms to cultivate educational design capabilities. It highlights deficiencies in existing research methods related to personalized matching and recommendation system design. To address these deficiencies, this study proposes a new set of adaptive matching methods. These methods include capability characteristic recognition based on competitive advantage thinking, the construction of individual strength models, as well as matching calculation, and decision- making scheme optimization using the projection decision method and the Hungarian method. Additionally, the study designs a learning project recommendation algorithm based on explicit ratings to enhance the accuracy and personalization of learning project recommendations. The application of these methods not only enhances the educational design capabilities of education students but also provides new theoretical support and practical guidance for the development of interactive, mobile, and adaptive learning platforms.