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- 저자 : Xiaoxiong Zhang (검색결과 5 건)
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Comparison of Tea Quality and Productive Effectiveness
Linfu Zhang,Huoxiang Ye,Yansheng Weng,Xiaoxiong Chen 한국차학회 2015 한국차학회지 Vol.- No.S
Mechanization processing of tea promotes development of Chinese tea industry, which makes tea manufacture from traditional style to modernization, and speeds up large scale of tea processing and increases economic efficiency of tea production. Techniques of tea processing are closely related with tea machines. During the experiments four kinds of processing methods were carried out and results showed that automatic Longjing tea processing machine got highest sensory score of tea quality and automatic processing line had the highest working efficient.
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An improved Kalman filter for joint estimation of structural states and unknown loadings
Jia He,Xiaoxiong Zhang,Naxin Dai 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.2
The classical Kalman filter (KF) provides a practical and efficient way for state estimation. It is, however, not applicable when the external excitations applied to the structures are unknown. Moreover, it is known the classical KF is only suitable for linear systems and can’t handle the nonlinear cases. The aim of this paper is to extend the classical KF approach to circumvent the aforementioned limitations for the joint estimation of structural states and the unknown inputs. On the basis of the scheme of the classical KF, analytical recursive solution of an improved KF approach is derived and presented. A revised form of observation equation is obtained basing on a projection matrix. The structural states and the unknown inputs are then simultaneously estimated with limited measurements in linear or nonlinear systems. The efficiency and accuracy of the proposed approach is verified via a five-story shear building, a simply supported beam, and three sorts of nonlinear hysteretic structures. The shaking table tests of a five-story building structure are also employed for the validation of the robustness of the proposed approach. Numerical and experimental results show that the proposed approach can not only satisfactorily estimate structural states, but also identify unknown loadings with acceptable accuracy for both linear and nonlinear systems.
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A two-stage Kalman filter for the identification of structural parameters with unknown loads
Jia He,Xiaoxiong Zhang,Zhouquan Feng,Zhengqing Chen,Zhang Cao 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.6
The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy.
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Huang Xiaoqian,Hamad Karki,Amit Shukla,Zhang Xiaoxiong 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
This paper presents an improved autonomous navigation control mechanism for a UAV for accurate tracking and inspection of the ground laid linear horizontal structures such as oil and gas pipelines. At first, the Canny Edge Detector (CED) and the Probabilistic Hough Transformation (PHT) are used to identify the structures based on visual data collected by the onboard camera. Then suitable geometrical parameters are extracted from the collected structure information to design controller mechanism of the UAV for autonomous tracking along the identified linear structure. For autonomous tracking, the development of an appropriate controller is significant because it affects the overall response time, sensitivity, accuracy of the tracking performance by the UAV. Through analyzing the previous research results of pipeline recognition and the UAV navigation by velocity mapping [1], it can be seen that the overall lateral correction of the navigation path based on the conventional PID has obvious disadvantages such as a slow response time, UAV oscillation, angular and lateral instability etc. Especially for a low-altitude tracking, the tracking target is prone to be out of camera view very quickly. Thus a new controller with variant coefficients is designed for a better tracking performance. Combined with the previous research achievement, this paper emphasizes the principal and the allocation of the newly designed variant PID controller.
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