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Xiangjian CHEN,Kun SHU,Di LI 한국항공우주학회 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2
In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and nonlinearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.
Modeling and Neuro-Fuzzy Adaptive Attitude Control for Eight-Rotor MAV
Xiangjian Chen,Di Li,Yue Bai,Zhijun Xu 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.6
This paper focuses on modeling and intelligent control of the new Eight-Rotor MAV which is used to solve the problem of low coefficient proportion between lift and gravity for Quadrotor MAV. The dynamical and kinematical modeling for the Eight-Rotor MAV was developed which has never been proposed before. Based on the achieved dynamic modeling, two types of controller were presented. One type, a PID controller is derived in a conventional way with simplified dynamics and turns out to be quite sensitive to sensor noise as well as external perturbation. The second type controller is the Neuro-Fuzzy adaptive controller which is composed of two type-II fuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, one of the T-IIFNNs is designed to learn the inverse model of Eight-Rotor MAV on-line, the other one is the copy of the former one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs are tuned on-line at the same time, and then the stability of the Eight-Rotor MAV closed-loop control system is proved using Lyapunov stability theory. Finally, the validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzy adaptive controller performs very well under sensor noise and external disturbances, and has more superiority than traditional PID controller.
Xiangjian Chen,Di Li,Hongmei Li 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11
This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM and PCM), the membership function is an interval distribution, the determined fuzzy values which are the outputs of the FCM and PCM. Secondly, the initialization of cluster center and the process of type-reduction are optimized in this algorithm, which can greatly reduce the calculation of IT2PFCM and accelerate the convergence of the algorithm. Finally, experimental results are given to show the effectives of proposed method in contrast to conventional FCM, PFCM and type 2 fuzzy c-means.
Optimal type II fuzzy neural network controller for Eight-Rotor MAV
Xiangjian CHEN,Di Li,Xi-Bei Yang,Yuecheng Yu 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4
This paper focuses on modeling and intelligent control of the new eight-rotor MAV which is used to solvethe problem of low coefficient proportion between lift and gravity for QuadrotorMAV. The dynamic and kinematicalmodeling for the eight-rotor MAV.Neuro-Fuzzy adaptive controller is proposed which is composed of two type-IIfuzzy neural networks (T-IIFNNs) and one PD controller: The PD controller is adopted to control the attitude, oneof the T-IIFNNs is designed to learn the inverse model of eight-rotor MAV on-line, the other one is the copy of theformer one to compensate for model errors and external disturbances, both structure and parameters of T-IIFNNs aretuned on-line at the same time, and then the stability of the eight-rotor MAV closed-loop control system is provedusing Lyapunov stability theory. Meanwhile ,in order to reduce the computation work, the type-reduction andmodel construction process have been improved. For the issue of type reduction, a novel improved EKM algorithmis developed for improving the EKM algorithm. The proposed algorithm provides two improvements on the EKMalgorithm. For the issue of rules redundant, the concept of normalized difference is proposed to describe the changeof adjacent singular value so as to reflect the essential differences between redundant rules and important rules. Then the number of effective singular can be determined according to its critical point, and the type-2 fuzzy modelis constructed with rules located by TLS decomposition. Finally, the validity of the proposed control method hasbeen verified through real-time experiments. The experimental results show that the performance of Neuro-Fuzzyadaptive controller performs very well under sensor noise and external disturbances.
Low-Complexity Watermarking into SAO Offsets for HEVC Videos
Xiangjian Wu,Hyun-Ho Jo,Donggyu Sim 대한전자공학회 2016 IEIE Transactions on Smart Processing & Computing Vol.5 No.4
This paper proposes a new watermarking algorithm to embed watermarks in thr process of sample adaptive offsets (SAO) for high efficiency video coding (HEVC) compressed videos. The proposed method embeds two-bit watermark into the SAO offsets for each coding tree unit (CTU). To minimize visual quality degradation caused by embedding watermark, watermark bits are embedded into SAO offset depending on the SAO types of block. Furthermore, the embedded watermark can be extracted by simply adding four offsets and checking their least significant bits (LSB) at the decoder side. The experimental results show that the proposed method achieves 0.3% BD-rate increase without much visual quality degradation. Two-bit watermark for each CTU is embedded for more bit watermarking. In addition, the proposed method requires negligible computational load for watermark insertion and extraction.
The implications of signaling lipids in cancer metastasis
Xiangjian Luo,Xu Zhao,Can Cheng,Namei Li,Ying Liu,Ya Cao 생화학분자생물학회 2018 Experimental and molecular medicine Vol.50 No.-
Metastasis is the most malignant stage of cancer. Lipid metabolic abnormalities are now increasingly recognized as characteristics of cancer cells. The accumulation of certain lipid species, such as signaling lipids, due to the avidity of lipid metabolism may be a causal factor of tumor malignant progression and metastatic behavior. In this review, we first describe signaling lipids implicated in cancer migration, invasion and metastasis. Next, we summarize the regulatory signaling hubs of lipid anabolic and catabolic metabolism. We then address lipid-rich circulating tumor cells (CTCs) and the lipid composition of exosomes budded off from tumor cells. We also present advances in targeting the regulatory hubs of lipid metabolism and signaling lipids in cancer therapy. Given the complexity of metabolic disorders in cancer, the development of significant portfolios of approaches to target signaling lipids by the integration of multiple chemical modulations, as well as molecular imaging modalities, should offer promising strategies for cancer therapy.
CHEN, Xiangjian,SHU, Kun,LI, Di The Korean Society for Aeronautical and Space Scie 2016 International Journal of Aeronautical and Space Sc Vol.17 No.2
In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.
FOA Based Diagnosis Model for Multivariate Production Process
Yang Mingshun,Kong Xiangjian,Gao Xinqin,Liuyong,Li Yan 보안공학연구지원센터(IJUNESST) 2015 International Journal of u- and e- Service, Scienc Vol.8 No.1
Fault diagnosis for quality control during the multivariate production process is widely used to detect abnormal fluctuations, find out failure reasons and take measures to maintain the stability of the production system accordingly. The neural network methodology has become a main method in the field of intelligent diagnosis recently. However, it has certain deficiencies such as longer training time, slower convergence rate and easier falling into a local optimal solution easily. As a result, the effect of fault diagnose is influenced. Thus, this article proposes the idea of using the Fruit Fly Optimization Algorithm in the multivariable process fault diagnosis model, at the same time, to analyze the out of control sample data in the automobile crankshaft production. Compared with the neural network model in dealing with the fault diagnosis in multivariate process, Fruit Fly Optimization Algorithm’s effectiveness s verified.