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.

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 m g) and developed pore structure, in which the pore diameter of micropores mainly concentrated in 0.

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.

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.

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.