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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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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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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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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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联合FOD-sCARS的土壤有机质高光谱机器学习估测模型 认领 引用 被引量：5
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作者 吴梦红 窦森 +5 位作者 林楠 姜然哲 陈思 李佳璇 付佳伟 梅显军 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2025年第1期204-212,共9页
土壤有机质(SOM)含量是表征土壤质量的关键指标,在全球碳循环系统中发挥重大作用。快速准确的SOM估算和空间制图对土壤碳库估算、作物生长监测和耕地规划管理具有重要意义。利用传统方法监测区域性SOM含量耗时费力,基于高光谱遥感影像建... 土壤有机质(SOM)含量是表征土壤质量的关键指标,在全球碳循环系统中发挥重大作用。快速准确的SOM估算和空间制图对土壤碳库估算、作物生长监测和耕地规划管理具有重要意义。利用传统方法监测区域性SOM含量耗时费力,基于高光谱遥感影像建立SOM估测模型是现在较为合理有效的方法。为探索解决目前高光谱遥感影像建立SOM含量估测模型存在光谱数据冗余、光谱数据特征提取精度低、小样本模型泛化能力不强的问题,选择位于青海省湟中县的研究区,共采集67个土壤样本。获取资源1号02D(ZY1-02D)高光谱遥感影像并进行预处理得到样点像元光谱数据,采用分数阶微分变换(FOD)方法挖掘与SOM含量具有响应关系的敏感波段,以0.2为一个步长,利用相关性阈值法对比分析不同阶次微分处理数据挖掘能力;运用稳定性竞争性自适应重加权采样算法(sCARS)去除高光谱冗余数据获取建模特征波段,选择随机森林(RF)、极端梯度提升树、极限学习机和岭回归机器学习作为建模算法,以全波段和特征波段光谱数据分别作为模型输入变量构建SOM估测模型进行高光谱反演研究工作;最后根据最优特征变量和建模算法,基于ZY1-02D遥感影像进行了SOM空间分布制图。结果表明:采用FOD变换相比整数阶可以大大提高波段与SOM含量间的相关性,挖掘出更多细微的与SOM含量产生响应关系的光谱波段,其中0.8阶微分变换效果最优,较原始波段相比相关系数最大值提高了0.546;相较于全波段光谱数据,采用sCARS特征提取方法获取特征波段构建模型的估测精度得到较大提升,说明sCARS可以有效提升建模数据的质量,提升模型预测精度。建模算法中RF表现最优,Rp2(模型决定系数)达到0.766,RPD达到1.86,较全波段建模结果Rp2提升约7.58%;基于FOD-sCARS和RF实现了区域SOM含量估测制图。研究进一步验证利用星载高光谱遥感影像是实现区域SOM估测制图的可靠途径,研究结果可为估测区域SOM含量提供新思路,为利用星载高光谱遥感影像绘制SOM含量空间分布图提供了数据支持。 展开更多
关键词 高光谱遥感影像 分数阶微分变换 稳定性竞争性自适应重加权采样算法 土壤有机质 随机森林
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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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基于CA/SPA-CARS算法的小麦条锈病特征波段优选与监测模型构建 认领 引用 被引量：1
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作者 谷玲霄 方涛 +4 位作者 杜林丹 吴喜芳 李长春 连增增 岳哲 《农业机械学报》 EI CAS CSCD 北大核心 2025年第6期487-498,共12页
作物病害会严重制约作物产量和品质,传统的病害监测方法效率低且易受主观因素影响。高光谱遥感技术以其高光谱分辨率和客观真实性在作物病害监测中展现出重要潜力。本文利用多生育期冬小麦地面高光谱及田间病情指数(Disease index,DI),... 作物病害会严重制约作物产量和品质,传统的病害监测方法效率低且易受主观因素影响。高光谱遥感技术以其高光谱分辨率和客观真实性在作物病害监测中展现出重要潜力。本文利用多生育期冬小麦地面高光谱及田间病情指数(Disease index,DI),基于相关性分析(Correlation analysis,CA)和连续投影法(Successive projections algorithm,SPA)分别对光谱数据进行光谱特征降维,通过构建最优参数的竞争性自适应重加权采样(Competitive adaptive reweighted sampling,CARS)算法优选小麦条锈病敏感波段,最后利用偏最小二乘回归(Partial least squares regression,PLSR)、反向传播神经网络(Back propagation neural network,BPNN)和极限学习机(Extreme learning machine,ELM)算法建立基于特征光谱的病情指数模型,比较不同建模方法的建模效果,实现小麦条锈病监测。研究结果表明,不同生育期均显示小麦条锈病敏感特征波段多集中于近红外和短波红外波段,其中挑旗期为842、850、858 nm,灌浆期为947、953、1275、1277、1590、1663、1665 nm;对比不同建模算法,PLSR模型表现最佳,满足小麦早期病虫害监测需求,且在病害中期显示更明显特征;挑旗期和灌浆期分别以SPA-CARS-MCX和CA-CARS-MSC数据构建PLSR模型预测效果最优,验证集R2分别为0.782和0.861,RMSE分别为0.022和0.094,RPD分别为2.140和2.687。本文构建算法能够为不同生育期小麦条锈病监测提供参考。 展开更多
关键词 小麦条锈病 光谱变换 特征波段选择 相关性分析 连续投影法 竞争性自适应重加权采样
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CARS算法提取特征光谱建立荞麦叶片总黄酮与蛋白质近红外模型 认领 引用
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作者 朱丽伟 杜千禧 +4 位作者 唐国红 李洪有 张晓娜 陈庆富 石桃雄 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2025年第9期2585-2589,共5页
为满足荞麦品质鉴定和育种工作的需要,采用竞争性自适应重加权采样算法(CARS)提取特征光谱,结合定量偏最小二乘法对荞麦叶片总黄酮和蛋白质含量进行了快速测定研究。首先利用Kennard-Stone(KS)算法划分训练集和测试集,训练集总黄酮含量... 为满足荞麦品质鉴定和育种工作的需要,采用竞争性自适应重加权采样算法(CARS)提取特征光谱,结合定量偏最小二乘法对荞麦叶片总黄酮和蛋白质含量进行了快速测定研究。首先利用Kennard-Stone(KS)算法划分训练集和测试集,训练集总黄酮含量的平均值、最大值和最小值含量分别是55.8、92.5和28.1 mg·g-1,测试集样品的平均值、最大值和最小值含量分别是71.0、99.8和31.5 mg·g-1。训练集蛋白质含量的平均值、最大值和最小值含量分别是169.6、331.0和121.2 mg·g-1,测试集样品蛋白质含量的平均值、最大值和最小值含量分别是158.2、183.0和129.1 mg·g-1。然后分别使用归一化、归一化+多元散射校正、归一化+标准正太变换、归一化+一阶导数、归一化+二阶导数、归一化+SG平滑滤波对波长在4000~12000 cm-1范围内光谱进行预处理,再采用CARS算法提取特征波段,最后利用偏最小二乘法建立预测模型。通过对模型训练集决定系数(Rc)、测试集决定系数(Rp)、交叉验证均方根误差(RMSECV)、测试集均方根误差(RMSEP)和剩余预测偏差(RPD)的综合分析,得到可预测荞麦总黄酮和蛋白质的最佳模型。其中3个总黄酮预测模型是可用的,最佳的预测模型使用了1102个波段中的46个特征波段,所使用的预处理方法为归一化+一阶导数,其模型的Rc、Rp、RMSECV、RMSEP和RPD分别为0.997、0.933、0.170、0.829和2.893。4个蛋白质预测模型是可用的,其最佳的预测模型使用了42个特征波长,所使用的预处理方法为归一化+二阶导数,其模型的Rc、Rp、RMSECV、RMSEP、RPD分别为0.998、0.965、0.202、0.353和3.849。结果表明,将KS算法和CARS算法应用到近红外光谱模型的建立过程,可以利用较少的样本建立可靠的预测模型,满足对荞麦叶片总黄酮和蛋白质的快速测定,为叶用荞麦育种工作提供有力的工具。 展开更多
关键词 近红外光谱 荞麦 KS算法 CARS算法 总黄酮 蛋白质
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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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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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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：3
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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：1
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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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面向紧急订单的汽车混流生产线调度优化 认领 引用 被引量：2
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作者 张雷 张开翔 《计算机集成制造系统》 EI CSCD 北大核心 2026年第1期159-173,共15页
在传统的面向紧急订单的汽车生产排序问题研究中,大多数研究仅聚焦于未上线车身的生产排序,对已上线车身的生产调度研究不足。本文提出了一种新型汽车排产调度模型,该模型在对未上线车身序列展开优化的同时,针对已上线车身引入一种动态... 在传统的面向紧急订单的汽车生产排序问题研究中,大多数研究仅聚焦于未上线车身的生产排序,对已上线车身的生产调度研究不足。本文提出了一种新型汽车排产调度模型,该模型在对未上线车身序列展开优化的同时,针对已上线车身引入一种动态重排序策略,旨在最大程度地降低紧急订单插入带来的负面影响。其次,基于生产过程碳排放、生产成本、配件消耗均衡和订单交付延误4个指标,采用一种新型的离散花授粉算法对面向紧急订单的汽车生产排序问题进行求解,通过与传统优化算法对比,证明了所提算法的优越性。最后,将该排产调度模型与“紧急订单优先”和“先来先服务”这两种汽车企业的常用方法进行比较,验证了所提方法对提升汽车企业应对紧急订单的灵活性和时效性水平的有效性和实用性。 展开更多
关键词 紧急订单 汽车排序 生产调度 花授粉算法
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地面高光谱耦合可解释性集成机器学习的农田土壤含盐量和pH反演 认领 引用 被引量：1
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作者 黄华雨 丁启东 +3 位作者 张俊华 周跃辉 潘鑫 贾科利 《环境科学》 EI CAS CSCD 北大核心 2026年第2期1080-1091,共12页
土壤盐碱化是制约农业可持续发展的关键因素.盐碱信息的及时获取对土壤改良与地力长效提升至关重要.以河套平原地面高光谱和实测土壤含盐量(SSC)及pH值为数据源,对高光谱反射率进行正交信号校正(OSC)变换后,利用竞争性自适应重加权采样(... 土壤盐碱化是制约农业可持续发展的关键因素.盐碱信息的及时获取对土壤改良与地力长效提升至关重要.以河套平原地面高光谱和实测土壤含盐量(SSC)及pH值为数据源,对高光谱反射率进行正交信号校正(OSC)变换后,利用竞争性自适应重加权采样(CARS)筛选盐碱信息的特征波段,并引入环境变量和微波遥感数据,基于极端梯度提升(XGBoost)、自适应提升(AdaBoost)和随机森林(RF)等6种集成机器学习算法,构建SSC和pH值的反演模型,并利用夏普利加性解释(SHAP)对模型进行可视化分析.结果表明:①河套平原农田土壤盐碱化等级整体呈轻、中程度,且盐化和碱化表现出较强的空间异质性.②OSC变换优化了光谱数据结构,使其在复杂背景下的解析能力显著增强;CARS有效筛选出与盐碱信息相关的特征波段,SSC特征波段包括450、470和600 nm等13个波长,pH特征波段包括680、730和740 nm等15个波长.③AdaBoost算法对SSC反演表现最优,验证集Rp2、均方根误差(RMSE)和相对分析误差(RPD)分别为0.852、1.352和2.88,而pH值则以XGBoost模型效果最佳,其Rp2、RMSE和RPD分别为0.908、0.151和3.31.④SHAP分析表明,SSC和pH值的预测模型体现了多因子协同作用.波段和气候因子为SSC建模的主导因素,累计贡献率达80.8%.土壤属性(24.88%)对pH值的建模贡献率最高,波段数据贡献率最小,为15.13%,微波遥感数据对盐碱信息建模贡献有限,多源数据组合为土壤盐碱化的精准监测提供了有力支撑.研究结论有助于推动土地可持续管理和农业高效生产. 展开更多
关键词 河套平原 地面高光谱 土壤含盐量 竞争性自适应重加权采样(CARS) 环境变量 夏普利加性解释
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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：2
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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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