Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with a...Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.展开更多
On January 17,the Chinese Association for International Understanding(CAFIU)and the Association of Bosnia-China Friendship co-hosted the webinar themed New Vision of China-Bosnia and Herzegovina Cooperation and Role o...On January 17,the Chinese Association for International Understanding(CAFIU)and the Association of Bosnia-China Friendship co-hosted the webinar themed New Vision of China-Bosnia and Herzegovina Cooperation and Role of Civil Society.展开更多
为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用...为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法对EI(Expected Improvement)函数寻优,解决了惩罚因子难以选择的问题,增强了算法的鲁棒性.采用2个数值算例和1个工程算例对算法进行测试的结果表明,改进后的EGO算法收敛精度更高,比较适合在工程中应用.展开更多
为了探究中国土地利用变化驱动机制和未来土地利用状况,该文利用中国科学院资源环境科学数据库中的2000年和2005年土地利用数据,结合区域土地利用变化与影响模型CLUE-S(the conversion of land use and its effects at small regional e...为了探究中国土地利用变化驱动机制和未来土地利用状况,该文利用中国科学院资源环境科学数据库中的2000年和2005年土地利用数据,结合区域土地利用变化与影响模型CLUE-S(the conversion of land use and its effects at small regional extent)和面向地理过程动态环境模型Dinamica EGO(environment for geoprocessing objects)模拟2000-2020年中国土地利用状况,并借助于Logistic回归结果和贝叶斯估计结果,探讨了中国2000-2005年土地利用适宜性和土地利用变化的驱动力空间特征。以2005年土地利用数据对模拟结果进行验证表明,CLUE-S模型和Dinamica EGO模型在LUCC预测上与实际结果一致性较好,并且CLUE-S模型在预测总体精度上优于Dinamica EGO模型。但在土地利用变化类型的数量预测上,Dinamica EGO模型的Markov过程可以准确预测,并且Dinamica EGO模拟的土地利用变化在空间分布上与经验结果较一致。从2020年中国土地利用预测结果来看,耕地、林地、水域和建设用地将会增加,草地会出现大面积的缩减,未利用地在CLUE-S模型预测中出现增加,而在Dinamica EGO模型中减少。该文可为国土资源规划和耕地资源保护政策的制定提供科学依据。展开更多
基金funded by DEANSHIP OF SCIENTIFIC RESEARCH AT UMM AL-QURA UNIVERSITY,Grant Number 22UQU4361009DSR04.
文摘Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.
文摘On January 17,the Chinese Association for International Understanding(CAFIU)and the Association of Bosnia-China Friendship co-hosted the webinar themed New Vision of China-Bosnia and Herzegovina Cooperation and Role of Civil Society.
文摘为了解决复杂工程优化问题计算量大的问题,提出了基于Kriging代理模型的改进EGO(Efficient Global Optimization)算法.采用小生境微种群遗传算法求解Kriging模型的相关向量,避免了模式搜索算法求解相关向量时对初始值的敏感性问题.采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法对EI(Expected Improvement)函数寻优,解决了惩罚因子难以选择的问题,增强了算法的鲁棒性.采用2个数值算例和1个工程算例对算法进行测试的结果表明,改进后的EGO算法收敛精度更高,比较适合在工程中应用.
文摘为了探究中国土地利用变化驱动机制和未来土地利用状况,该文利用中国科学院资源环境科学数据库中的2000年和2005年土地利用数据,结合区域土地利用变化与影响模型CLUE-S(the conversion of land use and its effects at small regional extent)和面向地理过程动态环境模型Dinamica EGO(environment for geoprocessing objects)模拟2000-2020年中国土地利用状况,并借助于Logistic回归结果和贝叶斯估计结果,探讨了中国2000-2005年土地利用适宜性和土地利用变化的驱动力空间特征。以2005年土地利用数据对模拟结果进行验证表明,CLUE-S模型和Dinamica EGO模型在LUCC预测上与实际结果一致性较好,并且CLUE-S模型在预测总体精度上优于Dinamica EGO模型。但在土地利用变化类型的数量预测上,Dinamica EGO模型的Markov过程可以准确预测,并且Dinamica EGO模拟的土地利用变化在空间分布上与经验结果较一致。从2020年中国土地利用预测结果来看,耕地、林地、水域和建设用地将会增加,草地会出现大面积的缩减,未利用地在CLUE-S模型预测中出现增加,而在Dinamica EGO模型中减少。该文可为国土资源规划和耕地资源保护政策的制定提供科学依据。
