Modulation Classification Using Compressed Sensing and Decision Tree-Support Vector Machine in Cognitive Radio System.

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree-support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO.

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm.

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Experimental Study and Numerical Analysis of the Seismic Performance of Glass-Fiber Reinforced Plastic Tube Ultra-High Performance Concrete Composite Columns.

To investigate the impact of the filament winding angle of glass-fiber reinforced plastic (GFRP) on the seismic behavior of GFRP tube ultra-high performance concrete (UHPC) composite columns, this study designs two types of GFRP tube UHPC composite columns. Quasi-static tests are conducted on the specimens subjected to horizontal reciprocating load and axial force, and the skeleton curve characteristics of the structure are analyzed. Furthermore, a finite element analysis model of the composite column is established to explore the effects of the diameter-thickness ratio, circumferential elastic modulus of confined tubes, and tensile strength of concrete on the seismic performance of the composite column. The analysis includes a review of the skeleton curve, energy dissipation capacity, and stiffness degradation of the structure under different designs. The results indicate that the use of GFRP tubes effectively enhances the seismic performance of UHPC columns. The failure mode, peak load, and peak displacement of the composite columns are improved. The finite element analysis results are in good agreement with the experimental results, validating the effectiveness of the analysis model. Extended analysis reveals that the bearing capacity of the specimen increases while the energy dissipation capacity decreases with a decrease in the diameter-thickness ratio and an increase in the circumferential elastic modulus. Although the tensile strength of concrete has some influence on the seismic performance of the specimen, its effect is relatively small. Through regression analysis, a formula for shear capacity suitable for GFRP tube UHPC composite columns is proposed. This formula provides a theoretical reference for the design and engineering practice of GFRP tube UHPC composite columns.

Investigation on pore structure regulation of activated carbon derived from sargassum and its application in supercapacitor.

In order to realize the effective regulation of the pore structure of activated carbon and optimize its pore structure properties as electrode material, the effects of activation temperature, activation time and impregnation ratio on the specific surface area, total pore volume and average pore diameter of activated carbon prepared by sargassum are studied by orthogonal experiment. In addition, the electrochemical properties of sargassum-based activated carbon (SAC) and the relationship between the gravimetric capacitance and specific surface area of SAC are also studied. The SACs prepared under all conditions have high specific surface area (≥ 2227 m2 g-1) and developed pore structure, in which the pore diameter of micropores mainly concentrated in 0.4 ~ 0.8 nm, the pore diameter of mesopores mainly concentrated in 3 ~ 4 nm, and the number of micropores is far more than that of mesopores. In the activation process, the impregnation ratio has the greatest effect on the specific surface area of SAC, the activation temperature and impregnation ratio have significant effect on the total pore volume of SAC, and the regulation of the average pore diameter of SAC is mainly realized by adjusting the activation temperature. The SACs exhibit typical electric double layer capacitance performances on supercapacitors, delivering superior gravimetric capacitance of 237.3 F g-1 in 6 mol L-1 KOH electrolyte system at current density of 0.5 A g-1 and excellent cycling stability of capacitance retention of 92% after 10,000 cycles. A good linear relationship between gravimetric capacitance and specific surface area of SAC is observed.