In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to co...In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.展开更多
This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be co...This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.展开更多
尽管传统的词袋(BoW,bag of worcls)模型在复杂场景行为识别中能够保持鲁棒性,但是硬向量量化会导致大量的近似误差,进而产生很差的特征集。行为识别中一个重要的挑战是视觉词汇的构造,从原始特征到分类标签没有直接的映射,因此高层的...尽管传统的词袋(BoW,bag of worcls)模型在复杂场景行为识别中能够保持鲁棒性,但是硬向量量化会导致大量的近似误差,进而产生很差的特征集。行为识别中一个重要的挑战是视觉词汇的构造,从原始特征到分类标签没有直接的映射,因此高层的视觉描述子需要更加精确的词典,故提出基于结构稀疏表示的人体行为识别方法。在所提出方法的:BoW模型中,视频表示为组稀疏编码系数的直方图。与传统的BoW模型相比,所提方法具有更少的量化误差,而且高层特征表示可以减少模型参数和存储复杂性,并在标准化的人体行为数据集上评价所提方法,数据集包括KTH,Weimann,UCF-Sports,UCF50人体行为数据集,实验结果表明,所提方法与现存的其他方法相比各方面性能都有显著的提高。展开更多
基金Supported by the National Natural Science Foundation of China (20476007, 20676013)
文摘In this study, Saccharomyces cerevisiae (baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.
基金Project (No. 2003 AA517020) supported by the Hi-Tech Researchand Development Program (863) of China
文摘This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.
文摘尽管传统的词袋(BoW,bag of worcls)模型在复杂场景行为识别中能够保持鲁棒性,但是硬向量量化会导致大量的近似误差,进而产生很差的特征集。行为识别中一个重要的挑战是视觉词汇的构造,从原始特征到分类标签没有直接的映射,因此高层的视觉描述子需要更加精确的词典,故提出基于结构稀疏表示的人体行为识别方法。在所提出方法的:BoW模型中,视频表示为组稀疏编码系数的直方图。与传统的BoW模型相比,所提方法具有更少的量化误差,而且高层特征表示可以减少模型参数和存储复杂性,并在标准化的人体行为数据集上评价所提方法,数据集包括KTH,Weimann,UCF-Sports,UCF50人体行为数据集,实验结果表明,所提方法与现存的其他方法相比各方面性能都有显著的提高。
