Power Control for Cognitive Radio Networks under Imperfect CSI Based on Game Theory

Guanglong Yang,Xiao Wang,Xuezhi Tan 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.12

In this paper, the problem of power control for cognitive systems is studied via game theory. The objective is to maximize the sum utility of secondary users (SUs) subject to the primary users (PUs) interference constraints, the transmission power constraints of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU. In our earlier work, the problem was formulated as a non-cooperative game under the assumption of perfect channel state information (CSI). Nash equilibrium (NE) is considered as the solution of this game. A distributed algorithm is proposed which can converge to the NE. Due to the limited cooperation between the secondary base station (SBS) and the PU, imperfect CSI between the SBS and the PU is further considered in this work. The problem is formulated as a robust game. As it is difficult to solve the optimization problem in this case, existence of the NE cannot be analyzed. Therefore, convergence property of the sum utility of SUs will be illustrated numerically. Simulation results show that under imperfect CSI the proposed approach can converge to a locally optimal transmission power level, while the sum utility of SUs converges with the increase of the transmission power constraint of SUs.

Optimal Relay Selection and Power Allocation in Cooperative Cognitive Communication System

Guanglong Yang,Xiao Wang,Xuezhi Tan 보안공학연구지원센터 2014 International Journal of Future Generation Communi Vol.7 No.6

This paper focuses on the optimal relay selection and power allocation issue, which studies the communication between SS (Secondary Source) and two SDs (Secondary Destinations) through single relay in CR (cooperative cognitive communication) system. Sufficiently takes the multi-antenna relay, primary-secondary users coexisting, and AF (Amplify and Forward) relay mode as the researching background, and takes the constraints caused by the primary user’s interference and the max transmission power into account, the author proposes to maximize the system throughput by the selection of cooperative relay and the allocation of optimal power. Furthermore, the author also puts forward the approximate expressions of the optimal power allocation, and figures out the optimal solution by the Lagrange multiplier, whose property can be manifested by the simulation and comparison results.

Robust Power Control Algorithm for Multiple Primary Users and Secondary Users in Cognitive Radio Networks

Guanglong Yang,Xiao Wang,Xuezhi Tan 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.11

Conventional distributed power control algorithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) which may lead to performance degradation in practical systems. In this paper, the robust distributed power control problem is investigated in CR systems by considering the uncertainty of channel gain. The objective is to maximize the sum rate of secondary users (SUs) subject to the primary users (PUs) interference power constraints and the transmission power constraint of SUs. The channel gain fluctuation is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints. Lagrangian duality method is applied to solve the robust power control problem in a distributed way. Simulation results show that the effectiveness of the proposed robust algorithm.

Mechanical and Interfacial Properties of Flax Fiber-reinforced Plastic Composites Based on a Chemical Modification Method

Hongguang Wang,HAO WU,Lanjie Yang,Guanglong Yu 한국섬유공학회 2020 Fibers and polymers Vol.21 No.7

Due to growing interest in environmental protection, eco-friendly fibers with high specific strength and low costare widely used to reinforce matrix materials. Flax fiber is a natural renewable plant fiber that originates from the phloem offlax. Flax fibers not only have the characteristics of other natural fibers but also have the outstanding mechanical properties ofnatural fibers. However, due to the large amount of hydrophilic hydroxyl groups on the surface, the hydrophilic absorption offlax fibers is large, and the wet-heat durability of the composite is poor, which limits the use of flax fiber as a reinforcementmaterial in civil engineering applications. In this study, based on the research results of flax fiber, a flax fiber sheet wasgrafted with multiwalled carbon nanotubes, a silane coupling agent and nano-TiO2 particles. The effect of different treatmentmethods and process parameters on the mechanical and interfacial properties of flax fiber-reinforced plastic (FFRP)composites materials was studied. The results show that the FFRP composites grafted with multiwalled carbon nanotubeshave higher tensile strength and interlaminar shear strength than those grafted with the same content of nano-TiO2 particles. Flax fiber composites with improved interfacial properties can be obtained by grafting flax fiber sheets, but the content ofparticles on the flax fiber surface should not exceed 2.0 wt.% of nano-TiO2 and multiwalled carbon nanotubes. Under thistreatment, the FFRP composites have improved properties, such as tensile strength, interlaminar shear strength and glasstransition temperature.

Effect of Cyclic Deep Cryogenic Treatment on Corrosion Resistance of 7075 Alloy

Ruiming Su,Siyi Ma,Kaining Wang,Guanglong Li,Yingdong Qu,Rongde Li 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.4

The cyclic deep cryogenic treatment was proposed to improve both the hardness and corrosion resistance of the high strength7075 aluminum alloy. The effect of different CDCT times on the exfoliation corrosion and intergranular corrosion of the alloyswere observed by scanning electron microscope. The corrosion behaviors of the alloys were monitored by electrochemicaltechniques. The hardness of the alloy was measured by Vickers hardness tester. Furthermore, the microstructures of thealloys were examined by transmission electron microscope. The results show that the corrosion resistance and hardness arestrongly affected by the precipitate state. The discontinuous grain boundary precipitates and the wide precipitate free zoneswill enhance the corrosion resistance. The fine precipitates distributed evenly in the matrix can increase the hardness. Afterthe CDCT, the corrosion resistance is remarkably improved without sacrificing the hardness. The best combination of thehardness and corrosion resistance is exhibited for the alloy treated with the CDCT twice.

Green Synthesis of Gold Nanoparticles Using Longan Polysaccharide and their Reduction of 4-nitrophenol and Biological Applications

Xiaoyu Zhang,Liyuan Fan,Yanshuai Cui,Tianming Cui,Shengfu Chen,Guanglong Ma,Wenlong Hou,Longgang Wang 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2020 NANO Vol.15 No.02

The green synthesis of gold nanoparticles (Au NPs) for catalytic and biological applications has been drawing great attention. To compare with plant extracts, the polysaccharides may be good reducing and stabilizing agents. In this work, we describe the preparation of longan polysaccharide stabilized gold nanoparticles (Aun-LP NPs) by reduction of gold ions using a green synthetic method. The formation of gold nanoparticles (Au NPs) was confirmed by UV-Vis spectra. TEM showed that Au NPs had a small size (7.8–15.6 nm) and were highly dispersed without any aggregation. XPS confirmed that the surface elemental composition of Aun-LP NPs was C, O, and Au. DLS demonstrated that Aun-LP NPs had good stability and negative zeta potential. In addition, Aun-LP NPs had high catalytic activity for the reduction of 4-nitrophenol. More importantly, Aun-LP NPs had ignorable cytotoxicity towards HeLa cells and showed good antioxidant activity. Taken together, the results indicated that longan polysaccharide can be used as reducing agents and stabilizers for the preparation of metallic nanoparticles, and the product had wide applications.

A new fault diagnosis method based on convolutional neural network and compressive sensing

Yunfei Ma,Xisheng Jia,Huajun Bai,Guozeng Liu,Guanglong Wang,Chiming Guo,Shuangchuan Wang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.11

Compressive sensing is an efficient machinery monitoring framework, which just needs to sample and store a small amount of observed signal. However, traditional reconstruction and fault detection methods cost great time and the accuracy is not satisfied. For this problem, a 1D convolutional neural network (CNN) is adopted here for fault diagnosis using the compressed signal. CNN replaces the reconstruction and fault detection processes and greatly improves the performance. Since the main information has been reserved in the compressed signal, the CNN is able to extract features from it automatically. The experiments on compressed gearbox signal demonstrated that CNN not only achieves better accuracy but also costs less time. The influencing factors of CNN have been discussed, and we compared the CNN with other classifiers. Moreover, the CNN model was also tested on bearing dataset from Case Western Reserve University. The proposed model achieves more than 90 % accuracy even for 50 % compressed signal.