The technology for beneficiation of banded iron ores containing low iron value is a challenging task due to increasing demand of quality iron ore in India. A flotation process has been developed to treat one such ore,...The technology for beneficiation of banded iron ores containing low iron value is a challenging task due to increasing demand of quality iron ore in India. A flotation process has been developed to treat one such ore, namely banded hematite quartzite （BHQ） containing 41.8wt% Fe and 41.5wt% SiO2,by using oleic acid, methyl isobutyl carbinol （MIBC）, and sodium silicate as the collector, frother, and dispersant, respectively. The relative effects of these variables have been evaluated in half-normal plots and Pareto charts using central composite rotatable design. A quadratic response model has been developed for both Fe grade and recovery and optimized within the experimental range. The optimum reagent dosages are found to be as follows： collector concentration of 243.58 g/t, dispersant concentration of 195.67 g/t, pH 8.69, and conditioning time of 4.8 min to achieve the maximum Fe grade of 64.25% with 67.33% recovery. The predictions of the model with regard to iron grade and recovery are in good agreement with the experimental results.展开更多
Evolutionary algorithm is time-consuming because of the large number of evolutions and much times of finite element analysis, when it is used to optimize the wing structure of a certain high altitude long endurance un...Evolutionary algorithm is time-consuming because of the large number of evolutions and much times of finite element analysis, when it is used to optimize the wing structure of a certain high altitude long endurance unmanned aviation vehicle（UAV）. In order to improve efficiency it is proposed to construct a model management framework to perform the multi-objective optimization design of wing structure. The sufficient accurate approximation models of objective and constraint functions in the wing structure optimization model are built when using the model management framework, therefore in the evolutionary algorithm a number of finite element analyses can he avoided and the satisfactory multi-objective optimization results of the wing structure of the high altitude long endurance UAV are obtained.展开更多
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation ...Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) and cloning process. This paper presents Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique in solving economic load dispatch problem. The combination creates a new algorithm that will be termed as Differential Evolution Immunized Ant Colony Optimization (DEIANT). DEIANT was utilized to optimize economic load dispatch problem. A comparison was made between DEIANT and classical ACO to evaluate the performance of the new algorithm. In realizing the effectiveness of the proposed technique, IEEE 57-Bus Reliable Test System (RTS) has been used as the test specimen. Results obtained from the study revealed that the proposed DEIANT has superior computation time.展开更多
为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶...为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶段增加反向学习,增强种群多样性.根据算法各个阶段不同特征引入衰减因子,平衡全局和局部勘探能力.选取8个高维函数和23个不同特征的优化函数对算法性能进行测试,进一步使用收敛性分析,寻优成功率,CPU时间,Wilcoxon秩和检验来评估改进算法,实验结果表明,ORGWO算法在求解高维问题上具有较好的精度,鲁棒性和更快的收敛速度.展开更多
安卓系统为浏览器分配资源时无法感知网页内容,会导致资源过度分配和电量不必要损失。同时,由于CPU可调节频率密度的增长,通过动态电压频率缩放(dynamic voltage and frequency scaling, DVFS)技术实现能耗优化的难度也随之增大。另外...安卓系统为浏览器分配资源时无法感知网页内容,会导致资源过度分配和电量不必要损失。同时,由于CPU可调节频率密度的增长,通过动态电压频率缩放(dynamic voltage and frequency scaling, DVFS)技术实现能耗优化的难度也随之增大。另外在系统默认的调控策略下,忽视了图形处理器(graphics processing unit, GPU)对浏览器运行的作用。针对上述问题,提出一种协同调控CPU和GPU实现功耗优化的方法。首先根据网页加载时处理器运行特征利用逻辑回归对网页进行分类,对网页特征加权实现复杂度量化,根据类别与复杂度采用DVFS技术限制CPU频率的同时调节GPU频率。该方法被应用于谷歌Pixel2 XL上的Chromium浏览器,对排名前500的中文网站进行测试,平均节省了12%功耗的同时减少了5%网页加载时间。展开更多
目的:为克服观察性研究中的混杂因素和反向因果关系的影响,通过两样本孟德尔随机化法探讨失眠与2型糖尿病之间的关联关系。方法:在欧洲裔人群最新的全基因组关联研究(genome-wide association study,GWAS)中选择与失眠密切相关的遗传位...目的:为克服观察性研究中的混杂因素和反向因果关系的影响,通过两样本孟德尔随机化法探讨失眠与2型糖尿病之间的关联关系。方法:在欧洲裔人群最新的全基因组关联研究(genome-wide association study,GWAS)中选择与失眠密切相关的遗传位点作为工具变量。剔除与吸烟、体育活动、饮酒、教育程度、肥胖或2型糖尿病显著相关的位点后,使用逆方差加权评估失眠对2型糖尿病的效应,并采用加权中位数法和MR-Egger回归分析来检验结果的稳健性。通过计算F统计量来检验工具变量的适用性,F统计量大于10认为存在弱工具变量偏倚可能性较小。采用MR-Egger回归进行多效性检验。此外,采用留一法(leave-one-out)进行敏感性分析,以进一步验证结果的稳定性和可靠性。结果:在全基因组水平上选择了248个与失眠独立相关的单核苷酸多态性(single nucleotide polymorphisms,SNPs)作为候选工具变量集合,基于千人基因组计划对候选工具变量集合进行修剪并剔除潜在的多效SNPs后,共纳入与失眠相关的167个SNPs作为最终的工具变量。本研究中F统计量为39.74,符合孟德尔随机化的相关性假设。逆方差加权法发现失眠与2型糖尿病的发生风险较高,在失眠的人群中发生2型糖尿病的风险是无失眠人群的1.14倍(95%CI:1.09~1.21,P<0.001)。加权中位数法和MR-Egger回归结果支持失眠对2型糖尿病存在正向关联。多效性检验表明结果受多效性影响的可能性较小,敏感性分析支持研究结果的可靠性与稳定性。结论:失眠是2型糖尿病的危险因素,失眠与2型糖尿病发病存在正向关联,本研究为糖尿病高危人群保持健康的生活方式提供了进一步的理论依据。展开更多
文摘The technology for beneficiation of banded iron ores containing low iron value is a challenging task due to increasing demand of quality iron ore in India. A flotation process has been developed to treat one such ore, namely banded hematite quartzite （BHQ） containing 41.8wt% Fe and 41.5wt% SiO2,by using oleic acid, methyl isobutyl carbinol （MIBC）, and sodium silicate as the collector, frother, and dispersant, respectively. The relative effects of these variables have been evaluated in half-normal plots and Pareto charts using central composite rotatable design. A quadratic response model has been developed for both Fe grade and recovery and optimized within the experimental range. The optimum reagent dosages are found to be as follows： collector concentration of 243.58 g/t, dispersant concentration of 195.67 g/t, pH 8.69, and conditioning time of 4.8 min to achieve the maximum Fe grade of 64.25% with 67.33% recovery. The predictions of the model with regard to iron grade and recovery are in good agreement with the experimental results.
文摘Evolutionary algorithm is time-consuming because of the large number of evolutions and much times of finite element analysis, when it is used to optimize the wing structure of a certain high altitude long endurance unmanned aviation vehicle（UAV）. In order to improve efficiency it is proposed to construct a model management framework to perform the multi-objective optimization design of wing structure. The sufficient accurate approximation models of objective and constraint functions in the wing structure optimization model are built when using the model management framework, therefore in the evolutionary algorithm a number of finite element analyses can he avoided and the satisfactory multi-objective optimization results of the wing structure of the high altitude long endurance UAV are obtained.
文摘Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
文摘Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) and cloning process. This paper presents Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique in solving economic load dispatch problem. The combination creates a new algorithm that will be termed as Differential Evolution Immunized Ant Colony Optimization (DEIANT). DEIANT was utilized to optimize economic load dispatch problem. A comparison was made between DEIANT and classical ACO to evaluate the performance of the new algorithm. In realizing the effectiveness of the proposed technique, IEEE 57-Bus Reliable Test System (RTS) has been used as the test specimen. Results obtained from the study revealed that the proposed DEIANT has superior computation time.
文摘为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶段增加反向学习,增强种群多样性.根据算法各个阶段不同特征引入衰减因子,平衡全局和局部勘探能力.选取8个高维函数和23个不同特征的优化函数对算法性能进行测试,进一步使用收敛性分析,寻优成功率,CPU时间,Wilcoxon秩和检验来评估改进算法,实验结果表明,ORGWO算法在求解高维问题上具有较好的精度,鲁棒性和更快的收敛速度.
文摘安卓系统为浏览器分配资源时无法感知网页内容,会导致资源过度分配和电量不必要损失。同时,由于CPU可调节频率密度的增长,通过动态电压频率缩放(dynamic voltage and frequency scaling, DVFS)技术实现能耗优化的难度也随之增大。另外在系统默认的调控策略下,忽视了图形处理器(graphics processing unit, GPU)对浏览器运行的作用。针对上述问题,提出一种协同调控CPU和GPU实现功耗优化的方法。首先根据网页加载时处理器运行特征利用逻辑回归对网页进行分类,对网页特征加权实现复杂度量化,根据类别与复杂度采用DVFS技术限制CPU频率的同时调节GPU频率。该方法被应用于谷歌Pixel2 XL上的Chromium浏览器,对排名前500的中文网站进行测试,平均节省了12%功耗的同时减少了5%网页加载时间。
文摘目的:为克服观察性研究中的混杂因素和反向因果关系的影响,通过两样本孟德尔随机化法探讨失眠与2型糖尿病之间的关联关系。方法:在欧洲裔人群最新的全基因组关联研究(genome-wide association study,GWAS)中选择与失眠密切相关的遗传位点作为工具变量。剔除与吸烟、体育活动、饮酒、教育程度、肥胖或2型糖尿病显著相关的位点后,使用逆方差加权评估失眠对2型糖尿病的效应,并采用加权中位数法和MR-Egger回归分析来检验结果的稳健性。通过计算F统计量来检验工具变量的适用性,F统计量大于10认为存在弱工具变量偏倚可能性较小。采用MR-Egger回归进行多效性检验。此外,采用留一法(leave-one-out)进行敏感性分析,以进一步验证结果的稳定性和可靠性。结果:在全基因组水平上选择了248个与失眠独立相关的单核苷酸多态性(single nucleotide polymorphisms,SNPs)作为候选工具变量集合,基于千人基因组计划对候选工具变量集合进行修剪并剔除潜在的多效SNPs后,共纳入与失眠相关的167个SNPs作为最终的工具变量。本研究中F统计量为39.74,符合孟德尔随机化的相关性假设。逆方差加权法发现失眠与2型糖尿病的发生风险较高,在失眠的人群中发生2型糖尿病的风险是无失眠人群的1.14倍(95%CI:1.09~1.21,P<0.001)。加权中位数法和MR-Egger回归结果支持失眠对2型糖尿病存在正向关联。多效性检验表明结果受多效性影响的可能性较小,敏感性分析支持研究结果的可靠性与稳定性。结论:失眠是2型糖尿病的危险因素,失眠与2型糖尿病发病存在正向关联,本研究为糖尿病高危人群保持健康的生活方式提供了进一步的理论依据。
