台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对...台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对台风预报信息进行动态插值。其次,基于Holland气压场模型和Batts梯度风模型构建融合物理信息的神经网络,将Holland模型和Batts模型中的经验参数替换成网络可学习的参数,并针对网络训练过程中可能出现的数值问题引入适当的近似方法。最后,对含时序模式注意力机制的长短期记忆网络(temporal pattern attention long short-term memory,TPA-LSTM)进行改进,嵌入融合物理信息的神经网络,利用近40年台风期间的数据进行训练和测试。结果表明,在引入较少参数的情况下,物理信息神经网络能减少TPA-LSTM网络的训练迭代次数以及提高预测精度,所提模型相比序列到序列(sequence to sequence,Seq2Seq)模型和TPA-LSTM网络具有更高的预测精度。展开更多
Obtaining comprehensive and accurate information is very important in intelligent tragic system （ITS）. In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this syst...Obtaining comprehensive and accurate information is very important in intelligent tragic system （ITS）. In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of tragic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation （BP） neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.展开更多
Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is conta...Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.展开更多
文摘台风条件下海上风电场风速变化大、无明显周期性,这对海上风电场的风速预测造成了极大的困难。针对此问题,提出台风条件下海上风电场风速多步预测方法。首先,针对台风预报信息与风电场风速数据时间尺度不统一的问题,提出用嵌入层网络对台风预报信息进行动态插值。其次,基于Holland气压场模型和Batts梯度风模型构建融合物理信息的神经网络,将Holland模型和Batts模型中的经验参数替换成网络可学习的参数,并针对网络训练过程中可能出现的数值问题引入适当的近似方法。最后,对含时序模式注意力机制的长短期记忆网络(temporal pattern attention long short-term memory,TPA-LSTM)进行改进,嵌入融合物理信息的神经网络,利用近40年台风期间的数据进行训练和测试。结果表明,在引入较少参数的情况下,物理信息神经网络能减少TPA-LSTM网络的训练迭代次数以及提高预测精度,所提模型相比序列到序列(sequence to sequence,Seq2Seq)模型和TPA-LSTM网络具有更高的预测精度。
基金funded by National Key Technology R&D Program of China (No.2006BAG01A03)
文摘Obtaining comprehensive and accurate information is very important in intelligent tragic system （ITS）. In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of tragic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation （BP） neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.
文摘Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.
