To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is ...To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.展开更多
乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析...乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。展开更多
In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the iss...Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.展开更多
基金supported by the National Natural Science Foundation of China(6107207061301179)the National Science and Technology Major Project(2010ZX03006-002-04)
文摘To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.
文摘乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
基金supported by the National Natural Science Foundation of China(6147115461876057)the Fundamental Research Funds for Central Universities(JZ2018YYPY0287)
文摘Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.
