Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN.

In the research of network abnormal traffic detection, in view of the characteristics of high dimensionality and redundancy in traffic data and the loss of original information caused by the pooling operation in the convolutional neural network, which leads to the problem of unsatisfactory detection effect, this paper proposes a network abnormal traffic detection algorithm based on RIC-SC-DeCN to improve the above problems. Firstly, a recursive information correlation (RIC) feature selection mechanism is proposed, which reduces data redundancy through the maximum information correlation feature selection algorithm and recursive feature elimination method. Secondly, a skip-connected deconvolutional neural network model (SC-DeCN) is proposed to reduce the information loss by reconstructing the input signal. Finally, the RIC mechanism and the SC-DeCN model are merged to form a network abnormal traffic detection algorithm based on RIC-SC-DeCN. The experimental results on the CIC-IDS-2017 dataset show that the RIC feature selection mechanism proposed in this paper has the highest accuracy when using MSCNN as the detection model compared to the other three, which can reach 96.22%. Compared with the other five models, the SC-DeCN model has the highest detection accuracy, while the model training time is moderate and can reach 96.55%. Compared with the SC-DeCN model, the RIC-SC-DeCN model reduces the overall training time by 45.50%, while the accuracy rate is increased to 97.68%. It shows that the algorithm proposed in this paper has a good detection effect in the detection of network abnormal traffic.

Research on an Identification Method for Gas Disaster Risk Based on the Selective Ensemble Classification Model.

To improve the accuracy of gas disaster risk identification, a selective ensemble classification model is proposed based on clustering selection and a new degree of combination fitness (CS-NDCF). First, nine base classifiers for gas disasters are constructed on the training data set, including the backpropagation (BP) neural network classifier, naive Bayes (NB) classifier, K-nearest neighbor (KNN) classifier, logistic regression (LR) classifier, decision tree (DT) classifier, support vector machine (SVM) classifier, SVM classifier with cross-validation (SVMCV), random forest (RF) classifier, and gradient boosting DT (GBDT) classifier. Second, the K-means clustering algorithm is used to cluster the base classifiers according to their classification performance. Then, the best performing classifier in each cluster is selected to compose the first selection set. Third, the degree of combination fitness is used to filter the first selection set again to obtain the optimal base classifier result set. Finally, an ensemble classification model is constructed with the optimal base classifier result set. The experimental results on actual mine monitoring data show that compared with the BP, NB, KNN, LR, DT, SVM, SVMCV, RF, and GBDT classifiers, the accuracy of CS-NDCF increases by 7.34, 34.83, 8.28, 12.94, 5.51, 11.72, 6.47, 1.31, and 1.20%, respectively, and CS-NDCF achieves the best forecasting results. Thus, CS-NDCF is an effective method for identifying gas disasters and has a good application value.

Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network.

To improve the utilization of mine gas concentration monitoring data with deep learning theory, we propose a gas concentration forecasting model with a bidirectional gated recurrent unit neural network (Adamax-BiGRU) using an adaptive moment estimation maximum (Adamax) optimization algorithm. First, we apply the Laida criterion and Lagrange interpolation to preprocess the gas concentration monitoring data. Then, the MSE is used as the loss function to determine the parameters of the hidden layer, hidden nodes, and iterations of the BiGRU model. Finally, the Adamax algorithm is used to optimize the BiGRU model to forecast the gas concentration. The experimental results show that compared with the recurrent neural network, LSTM, and gated recurrent unit (GRU) models, the error of the BiGRU model on the test set is reduced by 25.58, 12.53, and 3.01%, respectively. Compared with other optimization algorithms, the Adamax optimization algorithm achieved the best forecasting results. Thus, Adamax-BiGRU is an effective method to predict gas concentration values and has a good application value.

Water-processable polyaniline with covalently bonded single-walled carbon nanotubes: enhanced electrochromic properties and impedance analysis.

Hybrid electrochromic materials were readily synthesized via copolymerization of aniline with p-phenylenediamine-functionalized single-walled carbon nanotubes (SWCNTs) in the presence of poly(styrene sulfonate) (PSS) dopant in an aqueous medium. Polyaniline (PANI)-grafted SWCNTs are formed, and they are uniformly dispersed in the PANI/PSS matrix. Impedance analysis shows that the charge-transfer resistances of the hybrids at all states are reduced drastically with increasing SWCNT loading. With 0.8 wt % SWCNTs, the charge-transfer resistances of the hybrid at +1.5 and -1.5 V are only about 20% and 12% of those of PANI/PSS, respectively, which is due to the greatly increased redox reactivity given by the enhanced electron transport in the hybrid and further doping function of the SWCNTs. The remarkable increase in redox reactivity leads to much enhanced electrochromic contrast from 0.34 for PANI to 0.47 for PANI-SWCNT-0.8%.