Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited compreh...Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.展开更多
针对巡检机器人拍摄变电设备图像含噪严重、图像模糊和分辨率低影响设备缺陷检测的问题,提出一种基于条件生成式对抗网络和AlexNet-BiLSTM的变电设备缺陷检测方法,实现变电设备缺陷定位与辨识。首先,通过条件生成式对抗网络将模糊图像...针对巡检机器人拍摄变电设备图像含噪严重、图像模糊和分辨率低影响设备缺陷检测的问题,提出一种基于条件生成式对抗网络和AlexNet-BiLSTM的变电设备缺陷检测方法,实现变电设备缺陷定位与辨识。首先,通过条件生成式对抗网络将模糊图像转换成清晰图像;其次,为了避免大量超参数的设置,提高网络的训练速度,引入迁移学习思想,采用变电设备图像训练预训练的AlexNet网络,通过AlexNet网络提取图像的高维特征向量,利用双向长短时记忆网络(bi-directional long short-term memory, BiLSTM)对提取的特征向量进行分类;最后,在R-CNN框架下完成变电设备缺陷的标注和辨识。试验结果表明,所提方法复原的图像主观视觉效果良好,客观评价指标高,提高了变电设备缺陷检测准确率。展开更多
Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance ...Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.展开更多
针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后...针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后在处理好的环境变量训练数据集上进行训练,得到大棚环境变量预测模型;将LSTM模型与传统RNN和GRU预测模型进行对比实验。实验结果表明,LSTM模型的预测精度更高,鲁棒性更强,预测结果的均方根误差(root mean square error,RMSE)低于0. 05,可以实现大棚环境变量的准确预测,为大棚的智能控制提供可靠依据。展开更多
基金partly supported by Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowships for Research in Japan (P22370)by Key Project of Jiangsu Province (BE2022029) in China。
文摘Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.
文摘针对巡检机器人拍摄变电设备图像含噪严重、图像模糊和分辨率低影响设备缺陷检测的问题,提出一种基于条件生成式对抗网络和AlexNet-BiLSTM的变电设备缺陷检测方法,实现变电设备缺陷定位与辨识。首先,通过条件生成式对抗网络将模糊图像转换成清晰图像;其次,为了避免大量超参数的设置,提高网络的训练速度,引入迁移学习思想,采用变电设备图像训练预训练的AlexNet网络,通过AlexNet网络提取图像的高维特征向量,利用双向长短时记忆网络(bi-directional long short-term memory, BiLSTM)对提取的特征向量进行分类;最后,在R-CNN框架下完成变电设备缺陷的标注和辨识。试验结果表明,所提方法复原的图像主观视觉效果良好,客观评价指标高,提高了变电设备缺陷检测准确率。
文摘Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.
文摘针对温室大棚中环境变量变化趋势难以预测的问题,提出一种基于LSTM模型的大棚环境变量预测方法。首先根据实际采集到的大棚农作物西红柿生长环境变量(温度、湿度、二氧化碳浓度)的数据特点,设置网络模型隐藏层层数、调整网络参数;然后在处理好的环境变量训练数据集上进行训练,得到大棚环境变量预测模型;将LSTM模型与传统RNN和GRU预测模型进行对比实验。实验结果表明,LSTM模型的预测精度更高,鲁棒性更强,预测结果的均方根误差(root mean square error,RMSE)低于0. 05,可以实现大棚环境变量的准确预测,为大棚的智能控制提供可靠依据。
