The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which ...The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.展开更多
大规模的电动汽车(plug-in electric vehicle,PEV)和风力、太阳能等可再生能源(renewable energy sources,RES)发电并网使未来智能配电网规划需考虑更多不确定因素。在考虑PEV充电随机性和RES出力间歇性的基础上,利用机会约束规划法建...大规模的电动汽车(plug-in electric vehicle,PEV)和风力、太阳能等可再生能源(renewable energy sources,RES)发电并网使未来智能配电网规划需考虑更多不确定因素。在考虑PEV充电随机性和RES出力间歇性的基础上,利用机会约束规划法建立了计及环境成本、DG总费用和有功损耗的多目标分布式电源优化配置模型,并提出一种考虑随机变量相关性的拉丁超立方采样蒙特卡洛模拟嵌入纵横交叉算法(crisscross optimization algorithm-correlation Latin hypercube sampling Monte Carlo simulation,CSO-CLMCS)的方法对优化模型进行求解。该方法首先根据PEV和RES的概率模型及随机变量间的相关性,利用CLMCS概率潮流计算方法计算配电网概率潮流,并根据概率潮流结果检验约束条件及计算目标函数值,最后由CSO算法进行全局寻优得到最优配置方案。采用实际算例进行仿真,结果验证了所提模型和方法的可行性和有效性。展开更多
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185,62301367).
文摘The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
文摘大规模的电动汽车(plug-in electric vehicle,PEV)和风力、太阳能等可再生能源(renewable energy sources,RES)发电并网使未来智能配电网规划需考虑更多不确定因素。在考虑PEV充电随机性和RES出力间歇性的基础上,利用机会约束规划法建立了计及环境成本、DG总费用和有功损耗的多目标分布式电源优化配置模型,并提出一种考虑随机变量相关性的拉丁超立方采样蒙特卡洛模拟嵌入纵横交叉算法(crisscross optimization algorithm-correlation Latin hypercube sampling Monte Carlo simulation,CSO-CLMCS)的方法对优化模型进行求解。该方法首先根据PEV和RES的概率模型及随机变量间的相关性,利用CLMCS概率潮流计算方法计算配电网概率潮流,并根据概率潮流结果检验约束条件及计算目标函数值,最后由CSO算法进行全局寻优得到最优配置方案。采用实际算例进行仿真,结果验证了所提模型和方法的可行性和有效性。
