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Collaborative optimization of well operations and adjustment strategies in waterflooding reservoirs using an enhanced adaptive differential evolution algorithm 认领 引用
1
作者 Xian-Min Zhang Jian-Gang Yang +2 位作者 Qi-Hong Feng Ya-Wei Hou Lei Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2735-2757,共23页
Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This s... Efficient optimization of well operations andadjustment strategies in large-scale waterflooding reservoirs is a high-dimensional and complex challenge due to strong decision coupling and reservoir heterogeneity.This study proposes a collaborative optimization framework that integrates multiple adjustment strategies,includinginfillwell drilling,shut-in of low-efficiency wells,and injectionproduction well conversion.A penalty mechanism is introduced tobalance cumulative oil production maximization with minimum production constraints for infill wells.The core contribution is the development of a multi-strategy enhancedadaptive differential evolution algorithm(E-ADE),which incorporates the follower update mechanism of the SparrowSearch Algorithm(SSA)and the logarithmic spiral search strategy of the Whale Optimization Algorithm(WOA)into the differential evolution(DE)framework.By dynamically adjusting differential evolution vectors and adaptively regulating population size across optimization stages,E-ADE effectively balances global exploration and local exploitation,leading to significantlyimproved convergence speed and optimization accuracy.Benchmark tests on nine multimodalfunctions demonstrate that E-ADEconsistently outperforms classical algorithms,includingDE,GA,PSO,WOA,and SSA.The method is further applied to the PUNQ-S3 reservoir model and the S4 block of the W12-2 oilfield under high water-cut conditions.The results indicate that E-ADE enables adaptive optimization of infillwell placement,shut-in schemes,and welltype conversions,achieving coordinated improvements in both field-scale production andsingle-well performance,and substantially enhancing the efficiency of waterflooding development. 展开更多
关键词 Waterflooding reservoir Collaborative optimization Enhanced adaptive differential evolution algorithm Logarithmic spiral search Shannon entropy
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Millimeter wave imaging of Range Migration Algorithm with adaptive background filtering 认领 引用
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作者 CHENG Zhi-Hua ZHOU Ran +3 位作者 WANG Meng YU Tao WANG Yu-Lan YAO Jian-Quan 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2026年第2期279-284,共6页
This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proxi... This paper proposes a novel Range Migration Algorithm(RMA)integrated with an adaptive background filtering method specifically designed for near-field millimeter-wave imaging scenarios where targets are in close proximity to background structures.This method simulates the attention distribution mode of the human visual system which is used in Artificial Intelligence(AI)and called the Attention Mechanism.Based on the concept of static clutter filtering,the frequency-domain signals of the scanning aperture are divided into grid cells.Background scattering functions are established by analyzing the motion processes within each cell,and the background interference is linearly filtered out.An analysis of the manifestation of background scattering interference within the algorithm is carried out,and the impact of the grid cell dimension on the imaging quality is investigated.Experimental results show that the proposed method exhibits the capability to enhance the signal-to-noise ratio of both the target and the background.It effectively suppresses the background interference,leading to a more prominent image,meanwhile without imposing the excessive computational load.The method offers a novel solution for improving the performance of millimeter-wave imaging technology in practical applications. 展开更多
关键词 information processing technology millimeter-wave imaging Range Migration Algorithm(RMA) attention mechanism adaptive background filtering
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Efficient Adaptive Collision-Free Path Planning Algorithm for Indoor Mobile Robots:Optimization and Application 认领 引用
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作者 Xiangyang Lu Yandan Wang +1 位作者 Guangyi Zang Junqi Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期205-217,共13页
An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth... An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth paths,and poor dynamic obstacle avoidance capability.Through an“8+5”neighborhood screening,a 16-neighborhood evaluation function,and a second-order then third-order Bézier curve optimization process,a Jetson Nano+ROS(Robot Operating System)is deployed to meet the requirements of efficient and safe navigation for fire inspection robots in complex environments.The results show that,compared with the original algorithm,the proposed algorithm reduces the average number of traversed nodes by 49.23%,the number of turns in the optimized path has decreased by approximately 28.82%,decreases curvature by 66.6%,and eliminates path tangency with obstacles.This also supports real-time obstacle avoidance with integration DWA,and outperforms traditional methods. 展开更多
关键词 adaptive path planning improved A*algorithm Bézier curve optimization fire inspection robot Jetson Nano+ROS system
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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LLM‑assisted adaptive large neighborhood search for agile earth observation satellite scheduling 认领 引用
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作者 Feiran WANG Jiawei CHEN +4 位作者 Yonghao DU Yanjie SONG Yingwu CHEN Rammohan MALLIPEDDI Witold PEDRYCZ 《ENGINEERING Management》 CSCD 2026年第1期213-239,共27页
The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various cons... The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various constraints.In our study,we developed a mixed‑integer programming model for AEOSSP,incorporating key constraints related to visible time windows and time dependencies.To tackle this,we propose an Evolutionary Adaptive Large Neighborhood Search Algorithm(evALNS)enhanced by Large Language Models(LLMs).Our work pioneers the application of LLMs to ALNS by being the first to automatically develop and evolve its critical destroy heuristics.However,a naive application of LLMs is insufficient for such a complex domain.We therefore introduce a novel Dual‑Population Co‑Evolutionary Computing Framework(DPEC)to bridge the LLM’s knowledge gap by synergizing LLM‑generated heuristics with expert‑designed ones.This co‑evolution,guided by a Functional Natural Language Embedding(FNLE)strategy and customized prompts,significantly enhances the adaptability and efficiency of ALNS.Extensive numerical experiments demonstrated the superiority of the evALNS evolved under our framework,achieving an average profit improvement of 8.48%compared to the original ALNS with expert‑designed destroy operators. 展开更多
关键词 large language model algorithm design adaptive large neighborhood search satellite scheduling
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Anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs 认领 引用 被引量：2
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作者 Hui GAO Yuhong JIA +3 位作者 Liwen XU Fengxing PAN Shaowei LI Yaoming ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第9期283-303,共21页
For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collisi... For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A*have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A*(ARa A*),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A*features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A*to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A*outperforms Anytime Repairing A*(ARA*)and Anytime D*(AD*)in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A*can ensure the safe flight of quadrotors. 展开更多
关键词 Path planning Anytime algorithm Adaptive variable-step-size Suboptimality bound Unmanned aerial vehicle(UAV)
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Dimensional synchronous modeling-based enhanced Kriging algorithm and adaptive Copula method for multi-objective synthetical reliability analyses 认领 引用 被引量：1
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作者 Cheng LU Yunwen FENG +1 位作者 Chengwei FEI Da TENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第9期144-165,共22页
To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise mode... To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses. 展开更多
关键词 Adaptive Copula method Aeroengine turbine bladeddisc Aircraft landing gear system Correlation of multianalytical objectives Dimensional synchronous modeling-based enhanced Kriging algorithm Reliability analyses
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Dynamic Multi-Objective Gannet Optimization(DMGO):An Adaptive Algorithm for Efficient Data Replication in Cloud Systems 认领 引用
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作者 P.William Ved Prakash Mishra +3 位作者 Osamah Ibrahim Khalaf Arvind Mukundan Yogeesh N Riya Karmakar 《Computers, Materials & Continua》 SCIE EI 2025年第9期5133-5156,共24页
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat... Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance. 展开更多
关键词 Cloud computing data replication dynamic optimization multi-objective optimization gannet optimization algorithm adaptive algorithms resource efficiency scalability latency reduction energy-efficient computing
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Effect of Tooth Geometry on Multi-cycle Meshing Temperature of POM Worm Gears:Parametric Study via an Adaptive Iteration Algorithm 认领 引用
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作者 Kaixing Li Wujiao Xu +1 位作者 Yonggang Liu Datong Qin 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第2期427-439,共13页
Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycle... Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycles.However,the effects of tooth geometry parameters could manifest as the meshing cycles increase.This study investigated the effects of tooth geometry parameters on the multi-cycle meshing temperature of polyoxymethylene(POM)worm gears,aiming to control the meshing temperature elevation by tuning the tooth geometry.Firstly,a finite element(FE)model capable of separately calculating the heat generation and simulating the heat propagation was established.Moreover,an adaptive iteration algorithm was proposed within the FE framework to capture the influence of the heat generation variation from cycle to cycle.This algorithm proved to be feasible and highly efficient compared with experimental results from the literature and simulated results via the full-iteration algorithm.Multi-cycle meshing temperature analyses were conducted on a series of POM worm gears with different tooth geometry parameters.The results reveal that,within the range of 14.5°to 25°,a pressure angle of 25°is favorable for reducing the peak surface temperature and overall body temperature of POM worm gears,which influence flank wear and load-carrying capability,respectively.However,addendum modification should be weighed because it helps with load bearing but increases the risk of severe flank wear.This paper proposes an efficient iteration algorithm for multi-cycle meshing temperature analysis of polymer gears and proves the feasibility of controlling the meshing temperature elevation during multiple cycles by tuning tooth geometry. 展开更多
关键词 POM worm gears Multi-cycle meshing temperature Adaptive iteration algorithm Tooth geometry parameters Parametric study
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Adaptive Multi-Learning Cooperation Search Algorithm for Photovoltaic Model Parameter Identification 认领 引用
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作者 Xu Chen Shuai Wang Kaixun He 《Computers, Materials & Continua》 SCIE EI 2025年第10期1779-1806,共28页
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in... Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms. 展开更多
关键词 Photovoltaic model parameter identification cooperation search algorithm adaptive multiple learning chaotic grouping reflection
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A tracking algorithm based on adaptive Kalman filter with carrier-to-noise ratio estimation under solar radio bursts interference 认领 引用
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作者 ZHU Xuefen LI Ang +2 位作者 LUO Yimei LIN Mengying TU Gangyi 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期880-891,共12页
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers... Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance. 展开更多
关键词 solar radio burst(SRB) global positioning system(GPS) adaptive Kalman filter(AKF) tracking algorithm.
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Adaptive Meta-Loss Networks:Learning Task-Agnostic Loss Functions via Evolutionary Optimization 认领 引用
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作者 Mirna Yunita Xiabi Liu +1 位作者 Zhaoyang Hai Rachmat Muwardi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1931-1949,共19页
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc... Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability. 展开更多
关键词 Meta-learning adaptive loss function task-agnostic optimization evolutionary strategy genetic algorithm classification
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Enhanced Prostate Tumor Segmentation in MRI Using Hybrid Optimization and Adaptive Thresholding 认领 引用
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作者 Wafa Gtifa Ayoub Mhaouch Anis Sakly 《iRADIOLOGY》 CSCD 2026年第2期157-170,共14页
Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supe... Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supervised learning methods that require large annotated datasets and substantial computational resources,limiting their clinical applicability.This study aims to develop and evaluate a fully unsupervised framework for prostate tumor segmentation in multiparametric MRI using hybrid optimization and adaptive thresholding techniques.Methods:This study proposes an unsupervised prostate tumor segmentation framework based on hybrid optimization and adaptive thresholding.Two metaheuristic optimization algorithms,chaotic particle swarm optimization and forest optimization,were employed to optimize Otsu's variance-based thresholding and Kapur's entropy-based thresholding,resulting in four hybrid configurations.The framework was evaluated using multiparametric prostate MRI datasets,including apparent diffusion coefficient,T2-weighted,and diffusion-weighted imaging sequences.Segmentation performance was assessed using overlapbased and classification-based metrics.Statistical analysis included the computation of descriptive performance measures and confidence intervals to evaluate robustness and consistency across datasets.Results:The proposed framework demonstrated reliable and consistent segmentation performance across all MRI modalities.The Otsu-based hybrid configurations showed superior overlap and classification performance in diffusion-based imaging,whereas the entropy-based methods exhibited more conservative behavior on heterogeneous T2-weighted images.Overall,the optimization-based approaches achieved high segmentation accuracy and stability without the need for annotated training data.Conclusions:The proposed hybrid optimization and thresholding framework provides an effective,fully unsupervised solution for prostate tumor segmentation in multiparametric MRI.Its robustness,computational efficiency,and independence from training data highlight its potential for integration into clinical prostate cancer diagnostic workflows. 展开更多
关键词 adaptive thresholding magnetic resonance imaging optimization algorithms prostate cancer tumor segmentation
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Tilt Measurement Method of Wooden Columns in Traditional Timber Buildings Based on Adaptive RANSAC and PCA Method 认领 引用
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作者 Minyan Zhan Wei Yang +2 位作者 Minghao Wu Hsin-Yi Wang Yu-Hsien Ho 《Structural Durability & Health Monitoring》 EI 2026年第3期395-414,共20页
The inclination of wooden columns is a key indicator for evaluating the structural safety of traditional timber buildings in China.However,accurate measurement is challenging because these columns typically exhibit na... The inclination of wooden columns is a key indicator for evaluating the structural safety of traditional timber buildings in China.However,accurate measurement is challenging because these columns typically exhibit natural tapering,with diameters decreasing from the base to the top,and surface irregularities such as artificial cuts,cracks,and knots.Both the intrinsic geometric characteristics and surface defects reduce the precision of coordinate acquisition and the reliability of inclination estimation.To overcome these limitations,this study proposes a novel inclination measurement method for wooden columns in traditional timber buildings based on multi-section measurement and spatial line fitting.An adaptive random sample consensus algorithm is employed to effectively remove outliers induced by surface damage and measurement noise.Subsequently,principal component analysis is used to fit a three-dimensional spatial line through the center points of multiple cross-sections,enabling accurate computation of the inclination angle while accounting for structural imperfections.Moreover,the effects of damage defects,the number of measuring points,the measurement range,and the number of selected cross-sections on measurement accuracy are systematically investigated through statistical analysis,and corresponding recommended values for engineering practice are provided.The proposed method offers an efficient and reliable solution for inclination measurement,supporting the inspection and structural safety assessment of traditional timber columns. 展开更多
关键词 Traditional timber building inclination measurement of wooden column adaptive random sample consensus algorithm principal component analysis
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An Adaptive Trajectory-Assisted Dynamic Indoor Positioning Method Based on RSS Fingerprinting 认领 引用
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作者 Jing Liu Weijie Tan 《Computers, Materials & Continua》 SCIE EI 2026年第9期253-277,共25页
Due to its low hardware cost and ease of deployment,WiFi fingerprinting has become a prominent research direction in indoor positioning.However,traditional methods based on Received Signal Strength(RSS)still face thre... Due to its low hardware cost and ease of deployment,WiFi fingerprinting has become a prominent research direction in indoor positioning.However,traditional methods based on Received Signal Strength(RSS)still face three critical challenges:susceptibility to noise interference,lowretrieval efficiency as fingerprint databases scale up,and trajectory instability in dynamic environments.These challenges are inherently rooted in the stochastic fluctuation of RSS signals,the high-dimensional and non-Euclidean nature of fingerprint space,and the unpredictability of user movement patterns.To address these limitations,an adaptive trajectory-assisted dynamic indoor positioning algorithm based on RSS fingerprinting,termed AT-WKNN(Adaptive Trajectory-assisted Weighted K-Nearest Neighbor),is proposed.Specifically,a hybrid distance metric incorporating adaptive distance constraints is first designed to identify high-quality neighboring fingerprints while filtering out noisy samples,thereby improving positioning accuracy.Subsequently,aHierarchicalNavigable Small-World(HNSW)structure is employed to enable efficient fingerprint retrieval.In addition,a Kalman filter is utilized to smooth trajectory estimation and suppress dynamic noise.Experimental results demonstrate that the proposed AT-WKNN algorithm achieves a 43.0%improvement in positioning accuracy and a 6.90×increase in retrieval efficiency compared with the baseline WKNN method.Furthermore,validation on the large-scale UJIIndoorLoc benchmark dataset confirms the scalability and generalization capability of the proposed method. 展开更多
关键词 Indoor localization WiFi fingerprinting adaptive localization algorithm trajectory tracking
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Variable Projection Order Adaptive Filtering Algorithm for Self-interference Cancellation in Airborne Radars 认领 引用
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作者 LI Haorui GAO Ying +1 位作者 GUO Xinyu OU Shifeng 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第4期497-508,共12页
The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in... The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference. 展开更多
关键词 adaptive filtering algorithm airborne radar variable projection order mean squared deviation self-interference cancellation
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Novel algorithm of traffic signals adaptive control on urban roads intersections 认领 引用 被引量：1
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作者 Huanjiong Zhang 《Journal of Highway and Transportation Research and Development(English Edition)》 2025年第1期25-28,共4页
The control of traffic flows on urban roads intersections through the traffic control signals is very important,not only does the jammed traffic statement valuably relieve and the ratio of the traffic availability inc... The control of traffic flows on urban roads intersections through the traffic control signals is very important,not only does the jammed traffic statement valuably relieve and the ratio of the traffic availability increase,but also the traffic accidents evidently decrease.In this paper,an adaptive algorithm of traffic control signals on the urban roads intersections is designed in,this new algorithm can actively adjust the concrete control times of the traffic control signals based on the perceiving information of the waiting vehicles in real time,then the dynamic balance between the traffic control signals and the traffic flows can be realized.Furthermore,through experiment testing and demonstrating,this adaptive algorithm expresses some fine performances,it also shows good application prospect in the field of smart city. 展开更多
关键词 Traffic engineering Urban road intersections Traffic control signals Perception devices Adaptive algorithm Control phases
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OptimumMachine Learning on Gas Extraction and Production for Adaptive Negative Control 认领 引用 被引量：1
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作者 Cheng Cheng Xuan-Ping Gong +2 位作者 Xiao-Yu Cheng Lu Xiao Xing-Ying Ma 《Frontiers in Heat and Mass Transfer》 EI CAS 2025年第3期1037-1051,共15页
To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pur... To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume,which adversely impacts gas utilization efficiency in mines,a gas extraction pure volume prediction model was developed using Support Vector Regression(SVR)and Random Forest(RF),with hyperparameters fine-tuned via the Genetic Algorithm(GA).Building upon this,an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network,followed by field validation experiments.Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean absolute error,root mean square error,and mean absolute percentage error.In the extraction process of bedding boreholes,the influence of negative pressure on gas extraction concentration diminishes over time,yet it remains a critical factor in determining the extracted pure volume.In contrast,throughout the entire extraction period of cross-layer boreholes,both extracted pure volume and concentration exhibit pronounced sensitivity to fluctuations in extraction negative pressure.Field experiments demonstrated that the adaptive controlmodel enhanced the average extracted gas volume by 5.08% in the experimental borehole group compared to the control group during the later extraction stage,with a more pronounced increase of 7.15% in the first 15 days.The research findings offer essential technical support for the efficient utilization and long-term sustainable development of mine gas resources.The research findings offer essential technical support for gas disaster mitigation and the sustained,efficient utilization of mine gas. 展开更多
关键词 Gas extraction support vector regression(SVR) genetic algorithm hyperparameters fine-tuned negative pressure adaptive control
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AdaptiveMulti-Objective EnergyManagement Strategy Considering the Differentiated Demands of Distribution Networks with a High Proportion of New-Generation Sources and Loads 认领 引用 被引量：1
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作者 Huang Tan Haibo Yu +2 位作者 Tianyang Chen Hanjun Deng Yetong Hu 《Energy Engineering》 EI 2025年第5期1949-1973,共25页
With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev... With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy. 展开更多
关键词 High-proportion new-type source-loads multi-dimensional scenario classification clustering algorithms fuzzy logic control adaptive multi-objective energy management
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A new adaptive mutative scale chaos optimization algorithm and its application 认领 引用 被引量：23
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作者 Jiaqiang E Chunhua WANG +1 位作者 Yaonan WANG Jinke GONG 《控制理论与应用（英文版）》 2008年第2期141-145,共5页
Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with ... Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision. 展开更多
关键词 Adaptive Mutative scale Chaos optimization algorithm One-dimensional iterative chaotic self-map
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