Adaptive Method Research Articles

DAR-DRL: A dynamic adaptive routing method based on deep reinforcement learning

Mobile-centric wireless networks offer users a diverse range of services and experiences. However, existing intelligent routing methods often struggle to make suitable routing decisions during dynamic network changes, significantly limiting transmission performance. This paper proposes a dynamic adaptive routing method based on Deep Reinforcement Learning (DAR-DRL) to effectively address these challenges. First, to accurately model network state information in complex and dynamically changing routing tasks, we introduce a link-aware graph learning model (LA-GNN) that efficiently senses network information of varying structures through a hierarchical aggregated message-passing neural network. Second, to ensure routing reliability in dynamic environments, we design a hop-by-hop routing strategy featuring a large acceptance domain and a reliability guarantee reward function. This mechanism adaptively avoids routing holes and loops across various network scenarios while enhancing the robustness of routing under dynamic conditions. Experimental results demonstrate that the proposed DAR-DRL method achieves the network routing task with shorter end-to-end delays, lower packet loss rates, and higher throughput compared to existing mainstream methods across common dynamic network scenarios, including cases with dynamic traffic variations, random link failures (small topology changes), and significant topology alterations.

Extraction method for characterizing microcracks on Si3N4 ceramic bearings rolling element with contour segmentation

AbstractAiming at the fuzzy diffusion characteristics of microcrack regions in Si3N4 ceramic bearing rolling elements, an adaptive region segmentation method is proposed to achieve precise extraction of microcrack features. The fuzzy diffusion effect of the microcrack region is analyzed, and a structural complexity matrix is defined to represent the degree of structural variation in the image. A threshold point is determined through histogram analysis, and the matrix dispersion rate is calculated using a box plot to update the correction factor, optimizing the overlap of noise in the microcrack image. The causes of distortion in the microcrack region are analyzed, and pixel points are assigned based on spatial metrics and LAB color features. The fuzzy boundaries and overlapping regions are segmented using the fuzzy C‐means clustering algorithm. A grid search algorithm is embedded to quantitatively evaluate the optimal combination of hyperparameters, enabling comprehensive extraction of microcrack features in Si3N4 ceramic bearing rolling elements. The optimized image achieves an average peak signal‐to‐noise ratio of 39.3, an information fidelity criterion of 7.9328, and an average extraction accuracy of approximately 0.92, effectively overcoming the impact of the fuzzy diffusion effect on the accuracy of microcrack feature extraction in Si3N4 ceramic bearing rolling elements.

Analysis of Problems of Physical Education Teachers in Planning and Implementing Learning at State Senior High Schools in Southwest Aceh Regency

Physical education teachers are faced with challenges in carrying out an effective learning process. This requires teachers to have skills in designing adaptive learning methods. The lesson plan is a document that guides teachers in preparing the learning process. This research aims to determine the ability of physical education teachers to compile lesson plans and implement learning, as well as the problems faced by physical education teachers in the Public High School of Aceh Barat Daya Regency. The approach in this research is qualitative with a descriptive research type. The research subjects were 12 physical education teachers actively serving in the State High School of Southwest Aceh Regency. The data collection techniques used were observation, interviews, and documentation studies. Based on the research results, it can be concluded that 1) The ability of physical education teachers to prepare lesson plans at Senior High Schools in Aceh Barat Daya Regency has not been able to be implemented by the development of the applicable curriculum. Physical education teachers have not been able to design the objectives and stages of learning activities, as well as follow-up and assessment to determine the quality of the learning process, so it is limited to only one activity that is the same for all students. 2) The implementation of physical education learning at Senior High Schools in Aceh Barat Daya Regency has not been running optimally. Physical education teachers do not carry out systematic assessments. In addition, teachers do not apply cooling that is by the characteristics of the material being taught, and summarize the learning material by involving and guiding students. 3) The problems of physical education teachers in preparing learning plans and implementation at Senior High Schools in Aceh Barat Daya Regency are setting objectives formulating appropriate learning indicators and selecting learning methods to the conditions of facilities and infrastructure. Limitations in computer skills are also a problem for physical education teachers in preparing lesson plans. The availability of inadequate sports facilities makes it difficult to actively involve students in the learning process and to carry out accurate evaluations.

Addressing thermal challenges in hydrodynamic bearings: A contemporary review of strategies and technologies

Hydrodynamic bearings are integral components in various industrial sectors, including aerospace, defense technologies, and power generation units etc. Advancements in modern technology have driven the evolution of bearings to withstand the high temperatures, increased speeds, and additional load demands of contemporary operations. Some of the prime consequences of high temperatures include thermal deformation, misalignment, viscosity degradation, reduced oil film thickness, reduced load carrying capacity and premature failure due to bearing seizure. Researchers have rigorously addressed the persistent thermal challenges posed to hydrodynamic bearings, achieving significant advancements over the years. Key developments include innovative bearing pad designs, optimal materials, coating technologies, advanced computational software, novel lubrication methods, and efficient monitoring techniques. The introduction of materials with tailored properties such as high heat conductivity, high strength, and low-friction coatings has greatly enhanced bearing performance. Additionally, advanced computational software like ANSYS CFX, Fluent, and COMSOL, adaptive lubrication methods, and real-time temperature sensors have significantly improved the performance of critical systems, including hydro power plants. This review paper summarizes significant developments and investigations carried out to mitigate thermal effects in hydrodynamic bearings, and hence provides a comprehensive in depth details of the subject matter. Even though there has been a lot of progress in the field of hydrodynamic bearings, continuous research is important to tackle issues related to sustainability, industry-specific obstacles, developing technologies, and dynamic operating conditions.

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.

Bayesian control chart using variable sample size with engineering applications

In this study, we suggested an innovative approach by introducing an Adaptive Exponential Weighted Moving Average (AEWMA) control chart utilizing Variable Sample Size (VSS) under Bayesian methodology. The proposed methodology utilized an integer linear function to dynamically adjust sample sizes according to the AEWMA statistic. Another appealing feature of our adaptive framework is the integration of the smoothing constant of an EWMA chart, which enhances monitoring responsiveness. We reveal the superiority of our recommended control chart by extensive simulations to existing Bayesian EWMA and Bayesian AEWMA control charts using Fixed sample size (FSS). The offered Bayesian VAEWMA control chart is more sensitive to detection improvement, a decrease in the false alarm rate, and overall more effective than the existing methods. These findings provide additional justification for the basic notion that process control statistical tools needed to be dynamic, as the manufacturing process itself was dynamic. The results suggest the importance of introducing adaptive SPC methods in dynamic manufacturing environments. A real data application is performed to evaluate the validity and optimal performance of our recommended chart."Please check article if captured correctly."="Dear Editor we have checked and found corrrect."As per standard instruction, city is required for affiliations; however, this information is missing in affiliations [1, 5]. Please check if the provided city is correct and amend if necessary."Dear Editor we have checked and found correct. thanks youPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary."Dear Editor we have checked and found correct. thank you"

Flight Control Based on Adaptive Output Feedback for Quadrotors Using Quaternions

Quadrotors are expected to play an active role in out-of-sight areas such as equipment inspection and transportation of supplies to disaster areas. However, ensuring stable and highly accurate automatic flights has become a significant challenge. Trajectory tracking control methods that combine adaptive output feedback control based on almost strictly positive real characteristics and backstepping strategy have been proposed for a quadrotor control system with nonlinearities. These methods have simple structures and are robust. However, existing adaptive control methods have the problem of singularities, owing to the use of Euler angles in the modeling of quadrotors. In this paper, we propose an adaptive control method that enables a wider range of attitude changes using quaternions. The effectiveness of the proposed method is validated through numerical simulations.

A data-driven prediction for concrete crack propagation path based on deep learning method

Accurate and efficient prediction of concrete crack propagation path facilitates timely maintenance. To avoid errors arising from assumptions in physical models and parameter measurements, a data-driven prediction method for concrete crack propagation path is proposed. The spatio-temporal prediction approach is presented by unifying the conditional Generative Adversarial Network and Long Short-Term Memory Network (UcGLS-Net). The UcGLS-Net has the advantage of enabling temporal (by Long Short-Term Memory Network, LSTM) and spatial information (by the conditional Generative Adversarial Network, cGAN) in the concrete crack propagation process to be treated simultaneously and distinctly thus improving prediction accuracy. Notably, results demonstrate that UcGLS-Net still works in crack propagation path prediction even with unknown aggregate distribution. Moreover, the effects of historical input quantity and prediction intervals on prediction accuracy of the UcGLS-Net are studied and the network framework is optimized. Due to high efficiency, this study provides a new adaptable and real-time prediction method for concrete crack propagation path, offering support for the design, damage prediction, assessment, and maintenance of concrete structures.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Implementation of a community-based LC-UV drug checking service: promising preliminary findings on feasibility and validity

BackgroundThe increasing diversity of psychoactive substances on the unregulated drug market poses significant health, psychological, and social risks to people who use drugs (PWUD). To address these risks, various harm reduction (HR) policies have been implemented, including drug checking services (DCS). Many analytical methods are used for DCS. While qualitative methods (e.g., thin layer chromatography, spectroscopy) are easier to implement, they are not as accurate as quantitative methods (e.g., LC-UV, LC-MS). Some HR programmes have implemented high-performance liquid chromatography coupled with UV detection (LC-UV). This article presents the cross-validation of this quantitative method with a reference liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) method.MethodsDrug samples were provided by PWUD to a DCS called DrugLab in Marseille, France. The samples were weighed and prepared through dissolution in methanol, followed by ultrasonic bathing. Samples were analysed onsite using LC-UV analysis. They were then subsequently analysed with the reference LC-HRMS method. The LC-UV instrument in DrugLab was calibrated after being purchased; analysis of standard solutions was routinely performed once a month and after maintenance operations. For the LC-HRMS instrument, calibration and quality control procedures followed European Medicines Agency (EMA) guidelines. Statistical analyses were conducted including Spearman correlation tests using IBM® SPSS® Statistics version 20.ResultsA total of 102 samples representing different product classes and cutting agents were cross-validated. Differences between both analyses methods for each molecule analysed were ≤ 20%, with significant correlations between both methods’ results for most substances. Notably, LC-HRMS provided lower concentration values for cocaine and acetaminophen, whereas it provided higher values for other substances. Correlations were significant for cocaine, ketamine, MDMA, heroin, amphetamine, caffeine, acetaminophen, and levamisole.ConclusionsThis study demonstrates that the results provided by DrugLab were accurate and reliable, making LC-UV an adaptable, stable, and suitable analytical method for simple matrices like drugs in a DCS context. However, this cross validation does not guarantee accuracy over time. A proficiency test project in HR laboratories across France is currently under development in order to address potential drifts in LC-UV accuracy.

Research of Anomaly detection based on Dynamic Anomaly Detection Enhancement Framework

Abstract Anomaly detection plays a crucial role in various fields, from industrial defect inspection to geological detection. However, traditional approaches often struggle with insufficient discriminability and an inability to generalize to unseen anomalies. These limitations stem from the practical difficulty in gathering a comprehensive set of anomalies and the tendency to overlook anomalous instances in favor of normal samples. To address these challenges, we propose a novel Dynamic Anomaly Detection Enhancement Framework, integrating three key innovations: (1) SaliencyAug: An adaptive saliency-guided augmentation method that generates realistic pseudo-samples to enhance learning of rare anomalies, improving model generalization. (2) DynAB: A dynamic attention block that achieves effective multi-level feature fusion while minimizing redundant information, enhancing detection accuracy. (3) DualOM: A dual-head optimization module which employs separate heads for normal and anomalous sample learning, creating more explicit and discriminative decision boundaries. Extensive experiments across multiple real-world datasets demonstrate our framework's superior performance in detecting a wide range of anomalies, demonstrating 2.4% improvement over state-of-the-art methods.

Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network

With the presence of volatility and noise in the energy consumption data, existing energy consumption forecasting methods have difficulties in achieving satisfactory forecasting results from multiple perspectives, including accuracy, robustness, generalization, and efficiency. This study introduces a novel energy consumption forecasting framework that combines the adaptive signal decomposition method and iTransformer (ASSA-iTransformer) to tackle the above-mentioned difficulties. Each subsequence obtained by ASSA decomposition was fed into the iTransformer framework, and deep-level time series information was extracted. Compared to state-of-the-art methods across various evaluation dimensions, ASSA-iTransformer has improved the average mean absolute error (MAE) by 23.7%, root mean square error (RMSE) by 27.6%, and mean absolute percentage error (MAPE) by 16.4%. Moreover, the R2 values on five real-world household energy consumption datasets are close to 1, demonstrating overall outstanding performance.

Remote sensing and computer vision for marine aquaculture.

Aquaculture, the cultivation of aquatic plants and animals, has grown rapidly since the 1990s, but sparse, self-reported, and aggregated production data limit the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000 to 2021 including 4010 cages (average cage area, 69 square meters). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable, and adaptable method for monitoring aquaculture production from remote sensing imagery.

ADS–SLAM: a semantic SLAM based on adaptive motion compensation and semantic information for dynamic environments

Abstract Static environment assumptions are a prerequisite for simultaneous localization and mapping (SLAM), while interference from dynamic objects in the environment can seriously impair the system’s localization accuracy. Recently, many works have combined deep learning and geometric constraints to attenuate the interference of dynamic objects, but poor real-time performance and low accuracy in high dynamic scenes still exist. In this paper, we propose a semantic SLAM algorithm for complex dynamic scenes named ADS–SLAM. Our system combines the advantages of semantic information and motion constraints to remove dynamic points during tracking and localization. First, an adaptive dynamic point detection method based on epipolar constraint between adjacent frames is designed to adapt to the changes of object motion states and a motion area detection method based on Gaussian mixture model and Kalman Filter is utilized to effectively compensate the missed motion areas. Second, an object detection network with improved inference in the backend is utilized to extract prior object semantics. Lastly, the multi-level information is integrated in order to comprehensively screen all dynamic points in the environment and utilize only static points for pose estimation and optimization. Experimental evaluations on challenging public datasets and outdoor dynamic environments demonstrate that our algorithm achieves high localization accuracy in almost all dynamic scenarios compared to the current state-of-the-art SLAM algorithms, with the highest accuracy in high dynamic scenarios, and shows real-time performance for practical applications.

Force-compensation-based adaptive thermal shape correction method for XFEL optics

Force-compensation-based adaptive thermal shape correction method for XFEL optics

An adaptive assisted method based on MOPSO for THz MMA effective designing

Abstract To address the high time cost and low efficiency of traditional terahertz metamaterial absorber design, an adaptive method based on multi-objective particle swarm optimization was proposed. First, we presented a symmetric absorber of a four-split-ring resonator with a cross-shaped top layer. The structural parameters were optimized by the particle swarm optimization and multi-objective particle swarm optimization algorithms. The simulation results indicated that the multi-objective particle swarm optimization rapidly obtained geometric parameters that balance the high absorptivity and high quality factors. This method quickly identified a symmetric structure with absorptivity greater than 99% and quality factor of 377.45 at 1.584 THz. Additionally, the asymmetric structure achieved near-perfect absorption at 1.585 THz with a Q value of 281.32. By studying the electric field distribution and the surface current distribution, the physical mechanism of the absorber designed has been explained. This adaptive assisted method based on multi-objective particle swarm optimization can efficiently design metamaterial absorbers and offer an artificial intelligence strategy for terahertz functional device design.

Unlocking Insights: Exploring the Profiles of School-Based Telefacilitators.

School-based telefacilitators are critical yet underresearched members of telepractice programs. They are the face of the telepractice program and are often telepractice champions in their communities. However, we have little research explaining the typical profile of telefacilitators, making it more difficult to identify personnel characteristics that contribute to the implementation of sustainable telepractice programs. The purpose of this study was to describe the typical profile and preparation of school-based telefacilitators for physical, occupational, and speech therapy in the United States. A cross-sectional survey gathered 21 telefacilitator responses about their primary job title, preparation, confidence, and basic demographics. Survey recruitment followed an adaptive sampling method with three phases: stratified random sampling, purposive sampling, and targeted convenience sampling. Results were analyzed using frequency counts and descriptive statistics where appropriate. Most respondents held the primary job title of paraprofessional, but a diverse range of titles were reported, including superintendents and directors of special education. Half of the respondents received various methods of formal training for the role, whereas the other half were self-taught. Overall levels of confidence in job performance were high, yet self-taught telefacilitators felt that lack of training negatively impacted their performance. Most respondents had a bachelor's degree or higher. There is a high degree of variability in the profile and preparation of telefacilitators. Inconsistency in terminology and lack of agreed-upon job responsibilities are significant barriers to studying the role of telefacilitators and the ability to plan telepractice staffing needs adequately. Clinical implications and opportunities for future investigation are highlighted. https://doi.org/10.23641/asha.27115216.

An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

Characterization of the Epistemological, Methodological, and Pedagogical Perspective in English Language Teaching

This article explores the characterization of the epistemological, methodological, and pedagogical perspectives in English language teaching. Through a systematic literature review, the study identifies the theories of knowledge underpinning English learning, the most effective methodological approaches, and the pedagogical strategies that optimize the teaching-learning process. Findings suggest that integrating these perspectives promotes significant cognitive development, enhances communicative competence, and fosters student engagement and motivation. Additionally, the importance of adaptable pedagogical methods and the use of educational technologies to address individual student needs is highlighted. Practical recommendations for educators and areas for future research are proposed.