[Objectives]To study and optimize the process conditions of enzymatic hydrolysis technology for extracting polysaccharides from Pseudostellaria heterophylla fibrous roots and its application in workshop pilot tests.[M...[Objectives]To study and optimize the process conditions of enzymatic hydrolysis technology for extracting polysaccharides from Pseudostellaria heterophylla fibrous roots and its application in workshop pilot tests.[Methods]P.heterophylla fibrous roots were taken as the matrix material,and Box Behnken design was used to analyze the extraction time,composite enzyme addition amount,and liquid-solid ratio for response surface optimization experiments,and then applied to the pilot extraction of P.heterophylla fibrous roots.[Results]Response surface analysis showed that all factors had a significant impact on the experimental indicators.The optimal extraction process conditions for polysaccharides from P.heterophylla fibrous roots were extraction time of 2.7 h,compound enzyme addition of 2.5%,and liquid-solid ratio of 32.The yield of polysaccharides from P.heterophylla fibrous roots was 4.83%.The water extraction process of P.heterophylla fibrous roots extraction pilot was used as the control group for response surface optimization of the pilot experiment.The optimization results showed that the extraction time was 3 h,the amount of composite enzyme added was 2.5%,and the liquid-solid ratio was 28.The polysaccharide yield was 4.75%,an increase of 4.63%compared to the control group.[Conclusions]This paper could provide feasibility for the innovation of enzy-matic hydrolysis technology for P.heterophylla fibrous roots and its workshop pilot practice application,as well as a reference for the industrial application of its medicinal resources.展开更多
An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study.In order to reduce the number of d...An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study.In order to reduce the number of design variables for aerodynamic optimizations,the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters,the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output,and it is trained using the k-step contrastive divergence algorithm.The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils,and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models.Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization(PSO)framework,and applied to the robust aerodynamic design optimizations of Natural Laminar Flow(NLF)airfoil and transonic wing.The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing.By employing the PCA-DBN-based surrogate model,the developed efficient method improves the optimization efficiency obviously.展开更多
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
With the in-depth development of China's social system, cultivating students' ideological and political education is particularly important in university education. Ideological and political education is the f...With the in-depth development of China's social system, cultivating students' ideological and political education is particularly important in university education. Ideological and political education is the first lesson for college students to receive education. Correct thoughts and values play a very important role in talent cultivation. The cultivation of innovative talents requires, first of all, correct values and thoughts, and the ability to distinguish right from wrong. Only in this way can students know whether what they have done brings harm or benefits to the country when they make innovations in the later stage. Therefore, when carrying out ideological and political education, colleges and universities should constantly optimize ideological and political education work and cultivate high-quality innovative talents.展开更多
开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标...开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标漏检等问题,提出了一种UBA-OWDT(UCSO,BiStrip and AFDF of open-world detection transformer)开放世界目标检测网络。针对未知类召回率偏低的问题,对未知类评分优化(unknown class scoring optimization,UCSO),将生成的浅层类激活图与聚合类激活图融合,获取细粒度特征信息,提高未知类的目标评分,进而提升未知类的召回率;针对小目标漏检问题,将双条状注意力(spatial attention based on strip pooling and strip convolution,BiStrip)应用于输入特征图,捕获长程依赖,保留目标精确的位置信息,增强感兴趣目标的表征,以检测小目标;针对密集目标漏检问题,采用自适应特征动态融合(adaptive feature dynamic fusion,AFDF),根据目标大小和形状,获得不同的感受野,动态分配注意力权重,关注目标的重要部分,融合不同层级的特征,以检测密集目标。在OWOD数据集的实验结果表明,未知类召回率增值范围在0.7~1.5个百分点,mAP增值范围在0.6~1.2个百分点,与现有的开放世界目标检测方法相比,在召回率偏低、密集目标与小目标漏检问题上具有一定的优势。展开更多
基金Supported by Special Project of Central Leading Local Science and Technology Development(202113030)Regional Development Project of Fujian Provincial Science and Technology Plan(2022N3017).
文摘[Objectives]To study and optimize the process conditions of enzymatic hydrolysis technology for extracting polysaccharides from Pseudostellaria heterophylla fibrous roots and its application in workshop pilot tests.[Methods]P.heterophylla fibrous roots were taken as the matrix material,and Box Behnken design was used to analyze the extraction time,composite enzyme addition amount,and liquid-solid ratio for response surface optimization experiments,and then applied to the pilot extraction of P.heterophylla fibrous roots.[Results]Response surface analysis showed that all factors had a significant impact on the experimental indicators.The optimal extraction process conditions for polysaccharides from P.heterophylla fibrous roots were extraction time of 2.7 h,compound enzyme addition of 2.5%,and liquid-solid ratio of 32.The yield of polysaccharides from P.heterophylla fibrous roots was 4.83%.The water extraction process of P.heterophylla fibrous roots extraction pilot was used as the control group for response surface optimization of the pilot experiment.The optimization results showed that the extraction time was 3 h,the amount of composite enzyme added was 2.5%,and the liquid-solid ratio was 28.The polysaccharide yield was 4.75%,an increase of 4.63%compared to the control group.[Conclusions]This paper could provide feasibility for the innovation of enzy-matic hydrolysis technology for P.heterophylla fibrous roots and its workshop pilot practice application,as well as a reference for the industrial application of its medicinal resources.
基金co-supported by Aeronautical Science Foundation of China(No.2015ZBP9002)China Scholarship Council。
文摘An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study.In order to reduce the number of design variables for aerodynamic optimizations,the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters,the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output,and it is trained using the k-step contrastive divergence algorithm.The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils,and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models.Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization(PSO)framework,and applied to the robust aerodynamic design optimizations of Natural Laminar Flow(NLF)airfoil and transonic wing.The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing.By employing the PCA-DBN-based surrogate model,the developed efficient method improves the optimization efficiency obviously.
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With the in-depth development of China's social system, cultivating students' ideological and political education is particularly important in university education. Ideological and political education is the first lesson for college students to receive education. Correct thoughts and values play a very important role in talent cultivation. The cultivation of innovative talents requires, first of all, correct values and thoughts, and the ability to distinguish right from wrong. Only in this way can students know whether what they have done brings harm or benefits to the country when they make innovations in the later stage. Therefore, when carrying out ideological and political education, colleges and universities should constantly optimize ideological and political education work and cultivate high-quality innovative talents.
文摘开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标漏检等问题,提出了一种UBA-OWDT(UCSO,BiStrip and AFDF of open-world detection transformer)开放世界目标检测网络。针对未知类召回率偏低的问题,对未知类评分优化(unknown class scoring optimization,UCSO),将生成的浅层类激活图与聚合类激活图融合,获取细粒度特征信息,提高未知类的目标评分,进而提升未知类的召回率;针对小目标漏检问题,将双条状注意力(spatial attention based on strip pooling and strip convolution,BiStrip)应用于输入特征图,捕获长程依赖,保留目标精确的位置信息,增强感兴趣目标的表征,以检测小目标;针对密集目标漏检问题,采用自适应特征动态融合(adaptive feature dynamic fusion,AFDF),根据目标大小和形状,获得不同的感受野,动态分配注意力权重,关注目标的重要部分,融合不同层级的特征,以检测密集目标。在OWOD数据集的实验结果表明,未知类召回率增值范围在0.7~1.5个百分点,mAP增值范围在0.6~1.2个百分点,与现有的开放世界目标检测方法相比,在召回率偏低、密集目标与小目标漏检问题上具有一定的优势。
