Employing an ideal elasto-plastic model,the typically used strength reduction method reduced the strength of all soil elements of a slope.Therefore,this method was called the global strength reduction method(GSRM).How...Employing an ideal elasto-plastic model,the typically used strength reduction method reduced the strength of all soil elements of a slope.Therefore,this method was called the global strength reduction method(GSRM).However,the deformation field obtained by GSRM could not reflect the real deformation of a slope when the slope became unstable.For most slopes,failure occurs once the strength of some regional soil is sufficiently weakened; thus,the local strength reduction method(LSRM)was proposed to analyze slope stability.In contrast with GSRM,LSRM only reduces the strength of local soil,while the strength of other soil remains unchanged.Therefore,deformation by LSRM is more reasonable than that by GSRM.In addition,the accuracy of the slope's deformation depends on the constitutive model to a large degree,and the variable-modulus elasto-plastic model was thus adopted.This constitutive model was an improvement of the Duncan–Chang model,which modified soil's deformation modulus according to stress level,and it thus better reflected the plastic feature of soil.Most importantly,the parameters of the variable-modulus elasto-plastic model could be determined through in-situ tests,and parameters determination by plate loading test and pressuremeter test were introduced.Therefore,it is easy to put this model into practice.Finally,LSRM and the variable-modulus elasto-plastic model were used to analyze Egongdai ancient landslide.Safety factor,deformation field,and optimal reinforcement measures for Egongdai ancient landslide were obtained based on the proposed method.展开更多
In this article, authors introduce a method to assess local influence of obser- vations on the parameter estimates and prediction in multivariate regression model. The diagnostics under the perturbations of error vari...In this article, authors introduce a method to assess local influence of obser- vations on the parameter estimates and prediction in multivariate regression model. The diagnostics under the perturbations of error variance, response variables and explanatory variables are derived, and the results are compared with those of case- deletion. Two examples are analyzed for illustration.展开更多
Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model...Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.展开更多
超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local...超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。展开更多
针对移动机器人领域自适应蒙特卡洛定位算法(Adaptive Monte Carlo Localization,AMCL)在相似及变化场景下易失效的问题,本文提出基于改进YOLOv8构建语义链表为AMCL提供预定位位姿的方法,改变粒子权重更新方式,进而提升定位准确性和鲁棒...针对移动机器人领域自适应蒙特卡洛定位算法(Adaptive Monte Carlo Localization,AMCL)在相似及变化场景下易失效的问题,本文提出基于改进YOLOv8构建语义链表为AMCL提供预定位位姿的方法,改变粒子权重更新方式,进而提升定位准确性和鲁棒性.以YOLOv8为基础,结合信息聚集-分发机制和注意力尺度序列融合模块增强其Neck部分特征融合能力,并对模型进行剪枝,提升精度和速度;利用激光SLAM(Simultaneous Localization And Map-ping)构建二维栅格地图,通过改进的YOLOv8提取物体语义并映射到地图上,得到二维语义地图,根据各连续语义物体之间的关系构建语义链表;在定位过程中,将机器人识别到的物体语义信息与语义链表进行匹配,为AMCL提供预定位位姿,改变其粒子更新方式进行精确定位,并基于词袋模型降低免疫障碍物遮挡导致的语义链断裂.在相似及变化场景下进行定位对比实验,实验结果验证了本文算法的有效性.展开更多
传统的机器人定位导航方法在复杂建筑环境中存在精度不高、依赖传感器严重、无法有效处理动态障碍物等问题,导致其在实际应用中难以达到预期效果。为解决这些问题,引入了建筑信息模型(building information modeling,BIM)技术,借助BIM...传统的机器人定位导航方法在复杂建筑环境中存在精度不高、依赖传感器严重、无法有效处理动态障碍物等问题,导致其在实际应用中难以达到预期效果。为解决这些问题,引入了建筑信息模型(building information modeling,BIM)技术,借助BIM的几何和语义信息支持,在复杂环境中辅助机器人定位导航,为机器人提供更精确的环境感知和最优路径规划,减少与环境构件的碰撞风险,并提升任务执行的精准度和效率。比较论述了BIM技术在机器人定位、建图、路径规划等方面的应用现状,分析了其在建筑环境中的应用优势和挑战,并展望了未来在智能建筑和机器人智能化领域的应用前景。展开更多
基金Project([2005]205)supported by the Science and Technology Planning Project of Water Resources Department of Guangdong Province,ChinaProject(2012-7)supported by Guangdong Bureau of Highway Administration,ChinaProject(2012210020203)supported by the Fundamental Research Funds for the Central Universities,China
文摘Employing an ideal elasto-plastic model,the typically used strength reduction method reduced the strength of all soil elements of a slope.Therefore,this method was called the global strength reduction method(GSRM).However,the deformation field obtained by GSRM could not reflect the real deformation of a slope when the slope became unstable.For most slopes,failure occurs once the strength of some regional soil is sufficiently weakened; thus,the local strength reduction method(LSRM)was proposed to analyze slope stability.In contrast with GSRM,LSRM only reduces the strength of local soil,while the strength of other soil remains unchanged.Therefore,deformation by LSRM is more reasonable than that by GSRM.In addition,the accuracy of the slope's deformation depends on the constitutive model to a large degree,and the variable-modulus elasto-plastic model was thus adopted.This constitutive model was an improvement of the Duncan–Chang model,which modified soil's deformation modulus according to stress level,and it thus better reflected the plastic feature of soil.Most importantly,the parameters of the variable-modulus elasto-plastic model could be determined through in-situ tests,and parameters determination by plate loading test and pressuremeter test were introduced.Therefore,it is easy to put this model into practice.Finally,LSRM and the variable-modulus elasto-plastic model were used to analyze Egongdai ancient landslide.Safety factor,deformation field,and optimal reinforcement measures for Egongdai ancient landslide were obtained based on the proposed method.
文摘In this article, authors introduce a method to assess local influence of obser- vations on the parameter estimates and prediction in multivariate regression model. The diagnostics under the perturbations of error variance, response variables and explanatory variables are derived, and the results are compared with those of case- deletion. Two examples are analyzed for illustration.
基金supported by the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space(KF20202109)the National Natural Science Foundation of China(82004259)the Young Talent Training Project of Guangzhou University of Chinese Medicine(QNYC20190110).
文摘Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.
文摘超短期电力负荷预测作为电力系统的基本组成,能为生产调度计划的制定提供重要依据。然而,电力负荷具有非线性、时变性和不确定性,充分挖掘其潜在特征并分别预测,是提升预测准确性的关键。提出一种基于自适应局部迭代滤波(adaptive local iterative filtering,ALIF)的BiGRU-Attention-XGBoost电力负荷组合预测模型。该模型基于ALIF-SE实现将历史负荷序列分解重组为周期序列、波动序列和趋势序列;通过Attention机制对BiGRU模型进行改进,并结合XGBoost模型构建基于时变权重组合的电力负荷预测模型。实验分析表明,输入模型数据经过ALIF-SE处理后预测精度有明显提升;所提组合模型在工作日和节假日均具有较好的预测效果,预测误差大部分在5%以下;通过在不同负荷数据集下进行实验对比,验证了所提预测方法的可迁移性。实验结果证明,所提模型具有有效性、准确性和可行性。
文摘针对移动机器人领域自适应蒙特卡洛定位算法(Adaptive Monte Carlo Localization,AMCL)在相似及变化场景下易失效的问题,本文提出基于改进YOLOv8构建语义链表为AMCL提供预定位位姿的方法,改变粒子权重更新方式,进而提升定位准确性和鲁棒性.以YOLOv8为基础,结合信息聚集-分发机制和注意力尺度序列融合模块增强其Neck部分特征融合能力,并对模型进行剪枝,提升精度和速度;利用激光SLAM(Simultaneous Localization And Map-ping)构建二维栅格地图,通过改进的YOLOv8提取物体语义并映射到地图上,得到二维语义地图,根据各连续语义物体之间的关系构建语义链表;在定位过程中,将机器人识别到的物体语义信息与语义链表进行匹配,为AMCL提供预定位位姿,改变其粒子更新方式进行精确定位,并基于词袋模型降低免疫障碍物遮挡导致的语义链断裂.在相似及变化场景下进行定位对比实验,实验结果验证了本文算法的有效性.
文摘传统的机器人定位导航方法在复杂建筑环境中存在精度不高、依赖传感器严重、无法有效处理动态障碍物等问题,导致其在实际应用中难以达到预期效果。为解决这些问题,引入了建筑信息模型(building information modeling,BIM)技术,借助BIM的几何和语义信息支持,在复杂环境中辅助机器人定位导航,为机器人提供更精确的环境感知和最优路径规划,减少与环境构件的碰撞风险,并提升任务执行的精准度和效率。比较论述了BIM技术在机器人定位、建图、路径规划等方面的应用现状,分析了其在建筑环境中的应用优势和挑战,并展望了未来在智能建筑和机器人智能化领域的应用前景。
