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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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DCS-SOCP-SVM:A Novel Integrated Sampling and Classification Algorithm for Imbalanced Datasets 认领 引用
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作者 Xuewen Mu Bingcong Zhao 《Computers, Materials & Continua》 SCIE EI 2025年第5期2143-2159,共17页
When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes... When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets. 展开更多
关键词 DCS-SOCP-SVM imbalanced datasets sampling method ensemble method integrated algorithm
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Gradient Optimization Assisted Bubble Sampling Method for Structural Reliability Analysis 认领 引用
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作者 Zeng Meng LiwenjieShen +2 位作者 Changquan Li Jiaxiang Yi Gang Dong 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第6期891-904,共14页
The sampling method is an important numerical technique for solving reliability problems in engineering systems.However,the evaluation of the failure probability using classical sampling methods is time-consuming for ... The sampling method is an important numerical technique for solving reliability problems in engineering systems.However,the evaluation of the failure probability using classical sampling methods is time-consuming for complex engineering structure.To address this issue,this paper proposes a gradient optimization assisted bubble sampling method(GOBSM)to reduce the computational costs,which enhances the coverage range of bubbles,thereby improving the computational efficiency without sacrificing the accuracy.Furthermore,the bubble gradient iterative algorithm is developed to efficiently construct bubbles.Eight complex numerical examples are tested for assessing the failure probability,and the results demonstrate the performance of GOBSM. 展开更多
关键词 Reliability analysis Bubble sampling method Bubble gradient iterative algorithm Failure probability
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Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
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作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
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基于拉丁超立方(LHS)法的剪切增强型折纸蜂窝平台应力预测模型的研究 认领 引用
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作者 卞修亮 陈智威 +4 位作者 胡福 曹宏斌 陈华炜 李宸 蔡建国 《钢结构（中英文）》 CAS 2026年第1期39-46,共8页
蜂窝结构以轻量化、高吸能优势成为潜在防撞耗能芯材,将其应用在桥墩上可降低桥墩受驳船撞击风险的影响。剪切增强型蜂窝对普通蜂窝的各向压缩性能具有一定的补强有助于减小结构破坏,但目前对其关注较少;且传统设计依赖有限元模拟反复迭... 蜂窝结构以轻量化、高吸能优势成为潜在防撞耗能芯材,将其应用在桥墩上可降低桥墩受驳船撞击风险的影响。剪切增强型蜂窝对普通蜂窝的各向压缩性能具有一定的补强有助于减小结构破坏,但目前对其关注较少;且传统设计依赖有限元模拟反复迭代,计算效率偏低。为此,提出了一种基于改进拉丁超立方抽样(LHS)法的平台应力预测模型。首先,通过有限元模拟明确了剪切增强型折纸蜂窝在斜向撞击工况下的性能优势;其次,利用LHS法进行多参数空间均匀抽样,结合有限元计算获取高保真样本数据;进而,引入物理启发式函数,构建了包含壁厚、边长及偏移距离等关键参数的平台应力非线性预测模型;最后,通过随机样本对模型精度进行验证。结果表明,该预测模型的拟合决定系数R2达0.997,外推预测误差控制在10%以内。该研究通过LHS法与近似模型技术的结合,实现了剪切增强型折纸蜂窝平台应力的快速、精准预测,为结构的高效正向设计提供了关键技术支撑。 展开更多
关键词 剪切增强型蜂窝 拉丁超立方(LHS) 平台应力 有限元模拟 预测模型
Phase matching sampling algorithm for sampling rate reduction in time division multiplexing optical fiber sensor system 认领 引用
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作者 Junhui Wu Zhilin Xu +2 位作者 Yi Shi Yurong Liang Qizhen Sun 《Opto-Electronic Technology》 2025年第2期51-63,共13页
Time division multiplexing(TDM)architecture is an important approach to creating sensor arrays for massive scale monitoring.But it is paradoxical for the TDM interferometric sensor array to keep a short delay fiber fo... Time division multiplexing(TDM)architecture is an important approach to creating sensor arrays for massive scale monitoring.But it is paradoxical for the TDM interferometric sensor array to keep a short delay fiber for high sensing resolution and meanwhile use low sampling rate for practical applications.In this paper,a phase matching sampling(PMS)paradigm is proposed to address the above contradiction.By matching the phase of the sampling clock with the delay fiber length,combining with multiple-pulses sampling strategy,the proposed PMS method can avoid collecting the redundant information,facilitating the decreasing of sampling rate as well as delay fiber length of the TDM sensing system.The proof-of-concept experiments on an 8-channel TDM interferometric system demonstrate that when the sampling rate is fixed at 20 MS/s,by applying the PMS algorithm,the delay fiber length can be shortened from 100 m to 1 m,compared with applying the conventional sampling method.It reduced the phase noise of the system by a factor of 10 at 1 mHz and by a factor of 50 at 1 Hz.The PMS algorithm for greatly reducing the sampling rate is expected to fuel the TDM interferometric sensor arrays for many applications. 展开更多
关键词 time division multiplexing system sampling algorithm interferometric fiber optic sensors displacement sensing array
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A Novel Entropy-Based Framework for Hybrid Sampling in Imbalanced Learning 认领 引用
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作者 Ren-Jieh Kuo Muhammad Rizki +1 位作者 Ferani Eva Zulvia Eddy Roflin 《Computers, Materials & Continua》 SCIE EI 2026年第10期2294-2316,共23页
Imbalanced data remain a critical challenge in classification,as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes,which are often the most critical.To address this ... Imbalanced data remain a critical challenge in classification,as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes,which are often the most critical.To address this issue,this paper proposes the Information Filtered Hybrid Algorithm(IF-HA),a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory.IF-HA quantifies instance importance through an instance-wise difference statistic.In the undersampling stage,majority of instances with low difference statistics in the border area are eliminated,while in the oversampling stage,synthetic samples are generated from two minority core points or two minority instances with high difference statistics located in the border area.This process removes noise,eliminates redundant majority border points,and generates synthetic minority samples in informative regions until an entropy-based imbalance threshold is reached.The proposed algorithm is evaluated on 20 benchmark datasets from the UCI and KEEL repositories.Results demonstrate that IF-HA consistently improves minority detection and achieves higher F1 Scores,recall,and AUC(Area Under the Curve)than other methods,including SMOTE,Borderline-SMOTE,ADASYN(Adaptive Synthetic Sampling),and SMOTE-TLNN-DEPSO.A real-world tuberculosis(TB)dataset from Indonesia was further used to validate the practical applicability of IF-HA using KNN,Random Forest,and XGBoost(eXtreme Gradient Boosting)classifiers.The results show consistent improvements after applying IF-HA.These findings indicate that entropy-based hybrid sampling is a promising approach for structured tabular imbalanced classification,while further validation on high-dimensional text and image datasets remains necessary to establish broader generalizability. 展开更多
关键词 Imbalanced data hybrid sampling information theory entropy-based algorithm minority class detection tuberculosis classification
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Optimization of Process Parameters for Cracking Prevention of UHSS in Hot Stamping Based on Hammersley Sequence Sampling and Back Propagation Neural Network-Genetic Algorithm Mixed Methods 认领 引用 被引量：1
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作者 menghan wang zongmin yue lie meng 《Journal of Harbin Institute of Technology(New Series)》 CAS 2016年第2期31-39,共9页
In order to prevent cracking appeared in the work-piece during the hot stamping operation,this paper proposes a hybrid optimization method based on Hammersley sequence sampling( HSS),finite analysis,backpropagation( B... In order to prevent cracking appeared in the work-piece during the hot stamping operation,this paper proposes a hybrid optimization method based on Hammersley sequence sampling( HSS),finite analysis,backpropagation( BP) neural network and genetic algorithm( GA). The mechanical properties of high strength boron steel are characterized on the basis of uniaxial tensile test at elevated temperatures. The samples of process parameters are chosen via the HSS that encourages the exploration throughout the design space and hence achieves better discovery of possible global optimum in the solution space. Meanwhile, numerical simulation is carried out to predict the forming quality for the optimized design. A BP neural network model is developed to obtain the mathematical relationship between optimization goal and design variables,and genetic algorithm is used to optimize the process parameters. Finally,the results of numerical simulation are compared with those of production experiment to demonstrate that the optimization strategy proposed in the paper is feasible. 展开更多
关键词 hot stamping cracking Hammersley sequence sampling back-propagation genetic algorithm
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Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis 认领 引用
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作者 Xiukai YUAN Weiming ZHENG +1 位作者 Yunfei SHU Yiwei DONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第12期258-274,共17页
Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in th... Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously.To estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive Combination Augmented Line Sampling(ACALS)is proposed.It consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal combination.ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t.imprecise parameters.Then,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously.Finally,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF estimate.Overall,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V.Thus,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications.Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF. 展开更多
关键词 Time-variant reliability Imprecise reliability Line sampling Adaptive strategy Combination algorithm
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Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms 认领 引用
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作者 上官丹骅 包景东 《Communications in Theoretical Physics》 SCIE CAS 2010年第11期854-856,共3页
We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in... We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude. 展开更多
关键词 potential-decomposition strategy Markov chain Monte Carlo sampling algorithms
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Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
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作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
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基于LHS-WOA-ELM的隧道围岩参数反演分析 认领 引用 被引量：4
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作者 肖时辉 王峰 +3 位作者 何柏延 徐国元 黄伟真 李伟鹏 《矿业研究与开发》 CAS 北大核心 2025年第3期91-100,共10页
为提高隧道围岩力学参数取值的合理性,依托珠海市某超大断面隧道工程提出一种新型围岩参数反演模型。基于拉丁超立方体抽样(LHS)产生初始样本后进行参数敏感性分析以确定围岩的关键参数和改进样本结构,然后利用鲸鱼优化算法(WOA)对极限... 为提高隧道围岩力学参数取值的合理性,依托珠海市某超大断面隧道工程提出一种新型围岩参数反演模型。基于拉丁超立方体抽样(LHS)产生初始样本后进行参数敏感性分析以确定围岩的关键参数和改进样本结构,然后利用鲸鱼优化算法(WOA)对极限学习机(ELM)的隐含层神经元节点数、初始权重和阈值进行优化进而组成LHS-WOA-ELM反演模型,将反演所得参数代入FLAC3D计算位移并与现场实测数据进行对比分析。结果表明:采用基于LHS进行的参数敏感性分析能够以较少的样本考察多参数共同变化的情况,并确定影响围岩位移的主要参数为弹性模量E、黏聚力c和内摩擦角φ;相比于WOA、ELM、BP算法模型,LHS-WOA-ELM模型反演所获得的位移计算值与实测值相差更小,表明该反演分析方法能够很好地反映围岩参数与变形之间的非线性、不确定性特征,进一步提高围岩反演的精度和效率,可为地下硐室、矿业工程的设计参数确定提供参考。 展开更多
关键词 超大断面隧道 围岩参数反演 拉丁超立方体抽样 鲸鱼优化算法 极限学习机
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Application of a relief-optimized method for target space exteriorization sampling in landslide susceptibility assessment 认领 引用
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作者 CUI Yulong DENG Qining MIAO Haibo 《Journal of Mountain Science》 SCIE CSCD 2025年第9期3391-3407,共17页
Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological ... Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies. 展开更多
关键词 Non-landslide sample selection Relief algorithm Target Space Exteriorization Sampling Landslide Susceptibility Assessment
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Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System 认领 引用 被引量：2
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作者 Hongfeng Tao Dapeng Chen Huizhong Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第3期534-542,共9页
For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on sys... For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is proposed.Firstly,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method.Afterwards,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults adaptively.Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm. 展开更多
关键词 Equivalent fault model fault diagnosis iterative learning algorithm non-uniform sampling hybrid system virtual fault
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基于LHS-SSA-BPNN的地下厂房支护优化方法 认领 引用
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作者 陈雨婷 夏天倚 +3 位作者 徐云乾 包腾飞 程健悦 赵向宇 《水电能源科学》 北大核心 2025年第6期162-166,共5页
为解决传统地下厂房支护结构优化方法未考虑洞室交错的结构复杂性,以及统计回归模型难以定量地揭示支护参数与评价指标稳定性间复杂的映射关系、耗时长的问题,提出了一种基于拉丁超立方抽样方法(LHS),结合麻雀搜索算法(SSA)改进的反向... 为解决传统地下厂房支护结构优化方法未考虑洞室交错的结构复杂性,以及统计回归模型难以定量地揭示支护参数与评价指标稳定性间复杂的映射关系、耗时长的问题,提出了一种基于拉丁超立方抽样方法(LHS),结合麻雀搜索算法(SSA)改进的反向传播神经网络(BPNN)的地下厂房支护结构优化方法。该方法首先采用LHS构建样本方案,然后通过Python批量生成用于ABAQUS仿真分析的计算文件,接着将计算结果标准化成综合评价指标值作为学习样本,从锚杆长度和间距两个因素出发考虑支护参数对稳定性的影响,进一步利用SSA-BPNN构建支护参数与评价指标之间的非线性映射,最后用训练完成的SSA-BPNN模型在一定约束条件下的全局空间内搜索最优支护参数。实例分析表明,基于LHS-SSA-BPNN的支护结构优化方法能够准确搜索出最优支护参数,SSA-BPNN预测值与仿真分析结果的拟合度达96.16%,与BPNN相比性能明显提高,验证了该方法在复杂地质条件下地下厂房支护结构优化的优越性和合理性。 展开更多
关键词 地下厂房支护优化 拉丁超立方抽样 麻雀搜索算法 反向传播神经网络
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Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics 认领 引用
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作者 Jorg Peter Wilfried Klingert +5 位作者 Kathrin Klingert Karolin Thiel Daniel Wulff Alfred Konigsrainer Wolfgang Rosenstiel Martin Schenk 《World Journal of Critical Care Medicine》 2017年第3期172-178,共7页
AIM To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample dating.METHODS Blood pressure information obtained from a patient monitor was fe... AIM To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample dating.METHODS Blood pressure information obtained from a patient monitor was fed as a real-time data stream to an experimental medical framework. This framework was connected to an analytical application which observes changes in systolic, diastolic and mean pressure to determine anomalies in the continuous data stream. Detection was based on an increased mean blood pressure caused by the closing of the withdrawal three-way tap and an absence of systolic and diastolic measurements during this manipulation. For evaluation of the proposed algorithm, measured data from animal studies in healthy pigs were used.RESULTS Using this novel approach for processing real-time measurement data of arterial pressure monitoring, the exact time of blood withdrawal could be successfully detected retrospectively and in real-time. The algorithm was able to detect 422 of 434(97%) blood withdrawals for blood gas analysis in the retrospective analysis of 7 study trials. Additionally, 64 sampling events for other procedures like laboratory and activated clotting time analyses were detected. The proposed algorithm achieved a sensitivity of 0.97, a precision of 0.96 and an F1 score of 0.97.CONCLUSION Arterial blood pressure monitoring data can be used toperform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety. 展开更多
关键词 Blood withdrawal detection Sample dating algorithm Arterial blood gas analysis Patient monitoring Point-of-care diagnostics
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基于RANSAC与改进A*算法的果园移动机器人路径规划研究 认领 引用 被引量：2
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作者 王明之 吕强 +3 位作者 蒋杰 林刚 唐超 张皓杨 《西南大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第1期216-228,共13页
针对果园移动机器人在全局路径规划中存在的搜索时间长、安全性低、冗余节点多、路径不平滑以及行间作业精度不高等问题,研究提出一种基于RANSAC(Random Sample Consensus)算法与改进A*算法的路径规划方案。该方案首先利用RANSAC算... 针对果园移动机器人在全局路径规划中存在的搜索时间长、安全性低、冗余节点多、路径不平滑以及行间作业精度不高等问题,研究提出一种基于RANSAC(Random Sample Consensus)算法与改进A*算法的路径规划方案。该方案首先利用RANSAC算法拟合树行直线并提取果树行间中线,为后续改进A*算法提供最优中线参考路径;然后,在A*算法中引入中线栅格缩减策略,引导A*算法优先将中线作为最终路径;接着,对预估函数进行优化以提高运算效率,加入排斥力场函数以提升路径安全性;最后,结合安全距离阈值剔除冗余节点方法以消除多余节点,并采用三次均匀B样条曲线对路径进行平滑处理。在A*算法仿真对比试验中,本文改进A*算法相对于其他算法计算效率更高,生成路径更为安全平滑;在果园仿真栅格地图算法对比试验中,本文算法对于其他算法能规划出更高质量的行间中线路径;在模拟果园路径跟踪试验中,本文算法横向偏差均小于其他算法,适用性更强。 展开更多
关键词 移动机器人 路径规划 A*算法 随机抽样一致算法 果园
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考虑InSAR地表形变的滑坡易发性评价负样本选取与结果优化 认领 引用 被引量：4
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作者 秦佳松 李为乐 +3 位作者 周胜森 单云锋 何国庆 范禄震 《武汉大学学报（信息科学版）》 EI CAS CSCD 北大核心 2026年第6期1228-1241,共14页
滑坡易发性结果是滑坡灾害防控工作的重要参考,准确建模对灾害预警和风险管控至关重要。然而,在滑坡易发性建模过程中,非滑坡样本的选取存在较强的随机性,且未充分考虑滑坡负样本可能来源于潜在滑坡区域,导致评估结果的准确性受限。因此... 滑坡易发性结果是滑坡灾害防控工作的重要参考,准确建模对灾害预警和风险管控至关重要。然而,在滑坡易发性建模过程中,非滑坡样本的选取存在较强的随机性,且未充分考虑滑坡负样本可能来源于潜在滑坡区域,导致评估结果的准确性受限。因此,提出了一种基于合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)的地表形变信息滑坡负样本采样策略,以四川省汉源县为例,采用小基线集InSAR技术生成年平均地表形变速率,并从形变速率极低的区域中生成滑坡负样本。结果表明,在极端梯度提升和随机森林(random forest,RF)模型中,基于InSAR的采样方法均显著提升了模型的预测性能。其中,InSAR-RF模型的预测精度最高,受试者工作特征曲线下面积为0.847,准确率为89.8%,相比于传统的缓冲区采样方法,分别提高了5.5%和5.6%。此外,通过将InSAR数据引入模型优化滑坡易发性制图结果,并利用沙普利加性解释法解释模型内部的决策机制,进一步增强了滑坡易发性评价的科学性和可靠性。所提方法为InSAR技术在机器学习预测滑坡易发性中的应用提供了新思路。 展开更多
关键词 滑坡易发性 负样本 InSAR 极端梯度提升 随机森林 沙普利加性解释法
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基于“BPNN+NSGA-II”模型的简支梁优化算法研究 认领 引用 被引量：1
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作者 柏华军 潘昊阳 +1 位作者 肖祥 秦寰宇 《铁道标准设计》 北大核心 2026年第1期63-70,共8页
针对传统有限元法进行结构优化存在效率低的问题,通过对比不同代理模型和仿生优化算法特点,构建结构优化数学模型,研究BPNN神经网络和NSGA-II算法的架构原理及训练流程,并对比验证NSGA-II算法高效性和基于拉丁超立方设计(LHS)的采样方... 针对传统有限元法进行结构优化存在效率低的问题,通过对比不同代理模型和仿生优化算法特点,构建结构优化数学模型,研究BPNN神经网络和NSGA-II算法的架构原理及训练流程,并对比验证NSGA-II算法高效性和基于拉丁超立方设计(LHS)的采样方法优势,提出基于“BPNN+NSGA-II”模型的结构高效优化算法。其优化原理是基于有限元法构建的样本集对BPNN模型进行训练形成代理模型,使用NSGA-II算法对BPNN代理模型进行优化求解,形成“BPNN+NSGA-II”模型的高效优化算法。以某简支梁结构为例进行优化试验,结果表明:BPNN代理模型预测值与有限元模型计算值相比误差在2%以内,代理模型可靠性高;同时代理模型显著减少NSGA-II算法对有限元模型调用次数,提高优化效率。经优化的简支梁方案,承载能力安全系数接近规范限值,设计方案为近似最优方案。 展开更多
关键词 代理模型 优化算法 BPNN模型 NSGA-II算法 简支梁 拉丁超立方设计 蒙特卡罗采样
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多策略改进的蜣螂优化算法及其应用 认领 引用 被引量：3
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作者 陈禹 陈磊 黄凯阳 《无线电通信技术》 北大核心 2026年第1期212-224,共13页
为提升蜣螂优化(Dung Beetle Optimizer,DBO)算法的收敛速度与寻优精度,提出一种多策略改进的蜣螂优化(Multi-Strategy Improved DBO,MSIDBO)算法。使用最优拉丁超立方抽样初始化蜣螂位置,提高初始种群的多样性;将切线飞行策略与自适应... 为提升蜣螂优化(Dung Beetle Optimizer,DBO)算法的收敛速度与寻优精度,提出一种多策略改进的蜣螂优化(Multi-Strategy Improved DBO,MSIDBO)算法。使用最优拉丁超立方抽样初始化蜣螂位置,提高初始种群的多样性;将切线飞行策略与自适应惯性权重相结合并用于偷窃蜣螂的位置更新,协调算法的全局探索能力与局部开发能力;采用周期性跳跃机制,提高算法跳出局部最优的能力,进一步提升算法的整体寻优性能。采用12个基准测试函数进行仿真实验,实验结果表明,改进后的算法收敛速度更快,寻优精度更高、稳定性更好。将改进算法用于解决工程约束问题,进一步证明了改进算法的实用性。 展开更多
关键词 蜣螂优化算法 最优拉丁超立方抽样 切线飞行 自适应惯性权重 周期性跳跃机制
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