A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clo...A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.展开更多
The microkinetics of H_2S removal by ZnO desulfurization in H_2O-CO_2-N_2,H_2O-CO-N_2 and H_2O-O_2-N_2 gas mixtures was studied by thermogravimetric analysis. Experimentswere carried out with 100-120 mesh ZnO powder a...The microkinetics of H_2S removal by ZnO desulfurization in H_2O-CO_2-N_2,H_2O-CO-N_2 and H_2O-O_2-N_2 gas mixtures was studied by thermogravimetric analysis. Experimentswere carried out with 100-120 mesh ZnO powder at temperatures from 473 K to 563 K. The results showthat the kinetic behaviors of desulfurization could all be described by an improved shrinking-coremodel. The activation energies of the reaction and the diffusion in different gas atmosphere's wereestimated.展开更多
In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the...In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.展开更多
With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public securit...With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.展开更多
基金funded by the Key-Area Research and Development Program of Guangdong Province(Grant No.2020B1111200001)the Key project of monitoring,early warning and prevention of major natural disasters of China(Grant No.2019YFC1510304)+1 种基金the S&T Program of Hebei(Grant No.19275408D)the Scientific Research Projects of Weather Modification in Northwest China(Grant No.RYSY201905).
文摘A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.
文摘针对燃煤电厂在吹扫等过程中脱硫系统出口SO_(2)浓度的不能及时检测的问题,提出了一种基于特征选择的改进粒子群优化算法优化门控循环单元神经网络(IPSO-GRU)的脱硫系统出口SO_(2)浓度预测模型。通过最大相关最小冗余(minimum Redundancy and Maximum Relevance,mRMR)算法对采集的目标数据进行预处理,挑选出合适的变量,随后将选定的变量作为IPSO-GRU预测模型的输入。针对门控循环单元(Gated Recurrent Unit,GRU)模型关键超参数难以确定的问题,使用改进粒子群(Improved Particle Swarm Optimization,IPSO)算法对模型参数进行训练,以降低GRU的训练成本。最终实现对脱硫系统出口二氧化硫浓度的预测。实验结果表明,所提模型与传统循环神经网络相比预测精度更高,在工程实际中更具应用价值。
基金The author would like to express gratitude to the National Key Fundamental Research Project of Science and Technology(973)(No.G1999022104-1)with their financial aid.
文摘The microkinetics of H_2S removal by ZnO desulfurization in H_2O-CO_2-N_2,H_2O-CO-N_2 and H_2O-O_2-N_2 gas mixtures was studied by thermogravimetric analysis. Experimentswere carried out with 100-120 mesh ZnO powder at temperatures from 473 K to 563 K. The results showthat the kinetic behaviors of desulfurization could all be described by an improved shrinking-coremodel. The activation energies of the reaction and the diffusion in different gas atmosphere's wereestimated.
文摘In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.
基金This work was supported by Ministry of public security technology research program[Grant No.2020JSYJC22ok]Fundamental Research Funds for the Central Universities(No.2021JKF215)+1 种基金Open Research Fund of the Public Security Behavioral Science Laboratory,People’s Public Security University of China(2020SYS03)Police and people build/share a smart community(PJ13-201912-0525).
文摘With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.
