Institution
Southeast University
Education•Nanjing, China•
About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: MIMO & Control theory. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The adaptive supplementary control approach versus the traditional SMC in the cruising flight is verified, and three simulation studies are provided to illustrate the improved performance with the proposed approach.
Abstract: In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.
243 citations
••
TL;DR: It is found that AgNPs could inhibit the viability of AML cells including the isolates from AML patients, and supported the model that both generation of ROS and release of silver ions played critical roles in the AgnPs-induced cytotoxic effect against AMl cells.
243 citations
••
01 Dec 2016TL;DR: The concept of probabilistic linguistic preference relation (PLPR) is introduced to present the DMs preferences and an automatic optimization method is proposed to improve its consistency until acceptable.
Abstract: Display Omitted Propose the probabilistic linguistic preference relation (PLPR).Discuss the consistency of PLPR from the perspective of digraph.Present the consistency and acceptable consistency measures of PLPR.Establish an optimization model to improve the consistency of PLPR.Apply the proposed method to risk assessment. In recent years, the Belt and Road has aroused great attention of international society. It not only produces opportunities for China but also brings challenges: when Chinese investors invest to other countries, they will analyze the present situation of alternative countries and then assess the investment risk of these countries. Hence, how to assess the risk level of alternative countries correctly is pivotal. Moreover, affected by many factors such as decision makers (DMs) lacking of knowledge and the complexity of decision making problems, the DMs usually cannot use precise numbers to describe their preference information. Therefore, the use of linguistic variables is practical. As a type of linguistic term set, the probabilistic linguistic term set (PLTS) can reflect different importance degrees or weights of all possible evaluation values of a specific object. Whats more, when the DMs use linguistic variables to express their judgements, they sometimes cannot give their evaluation values for attributes directly. In such a case, the DMs usually provide their judgements by pairwise comparison of alternatives. In this paper, we introduce the concept of probabilistic linguistic preference relation (PLPR) to present the DMs preferences. The additive consistency of the PLPR is discussed from the perspective of the preference relation graph. Then, the consistency index of the PLPR is defined to measure the consistency. We also introduce the acceptable consistency of the PLPR. Moreover, as for the unacceptable consistent PLPR, an automatic optimization method is proposed to improve its consistency until acceptable. Once all the PLPRs are of acceptable consistency, we directly use the aggregation operators to obtain the comprehensive preference values of alternatives and then rank the alternatives according to the derived preference values. Finally, an application example involving the Belt and Road is given and the discussion about the results is conducted.
243 citations
••
TL;DR: A novel strategy to address the problem of fast capacity decay caused by polysulfide dissolution/shuttling and low specific capacity caused by the poor electrical conductivities of the active materials is demonstrated by designing and synthesizing a carbon nanotube (CNT)/NiFe2O4-S ternary hybrid material structure.
Abstract: The rechargeable lithium–sulfur battery is a promising option for energy storage applications because of its low cost and high energy density. The electrochemical performance of the sulfur cathode, however, is substantially compromised because of fast capacity decay caused by polysulfide dissolution/shuttling and low specific capacity caused by the poor electrical conductivities of the active materials. Herein we demonstrate a novel strategy to address these two problems by designing and synthesizing a carbon nanotube (CNT)/NiFe2O4–S ternary hybrid material structure. In this unique material architecture, each component synergistically serves a specific purpose: The porous CNT network provides fast electron conduction paths and structural stability. The NiFe2O4 nanosheets afford strong binding sites for trapping polysulfide intermediates. The fine S nanoparticles well-distributed on the CNT/NiFe2O4 scaffold facilitate fast Li+ storage and release for energy delivery. The hybrid material exhibits balanced ...
243 citations
••
TL;DR: An end- to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR) and can outperform the feature-based method, and obtain a closer approximation to the optimal ML- AMC.
Abstract: Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. Conventional AMCs can be categorized into maximum likelihood (ML) based (ML-AMC) and feature-based AMC. However, the practical deployment of ML-AMCs is difficult due to its high computational complexity, and the manually extracted features require expert knowledge. Therefore, an end-to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR). With CNN-AMC, a unit classifier is adopted to accommodate the varying input dimensions. The direct training of CNN-AMC is challenging with the complicated model and complex tasks, so a novel two-step training is proposed, and the transfer learning is also introduced to improve the efficiency of retraining. Different digital modulation schemes have been considered in distinct scenarios, and the simulation results show that the CNN-AMC can outperform the feature-based method, and obtain a closer approximation to the optimal ML-AMC. Besides, CNN-AMCs have the certain robustness to estimation error on carrier phase offset and SNR. With parallel computation, the deep-learning-based approach is about $ 40$ to $ 1700$ times faster than the ML-AMC regarding inference speed.
243 citations
Authors
Showing all 66906 results
Name | H-index | Papers | Citations |
---|---|---|---|
H. S. Chen | 179 | 2401 | 178529 |
Yang Yang | 171 | 2644 | 153049 |
Gang Chen | 167 | 3372 | 149819 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Yi Yang | 143 | 2456 | 92268 |
Guanrong Chen | 141 | 1652 | 92218 |
Wei Huang | 139 | 2417 | 93522 |
Jun Chen | 136 | 1856 | 77368 |
Jian Li | 133 | 2863 | 87131 |
Xiaoou Tang | 132 | 553 | 94555 |
Zhen Li | 127 | 1712 | 71351 |
Tao Zhang | 123 | 2772 | 83866 |
Bo Wang | 119 | 2905 | 84863 |
Jinde Cao | 117 | 1430 | 57881 |