ORCID Profile
0000-0002-7738-0078
Current Organisation
University of Tasmania
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 10-09-2014
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2002
Publisher: IEEE
Date: 10-2013
Publisher: Springer Science and Business Media LLC
Date: 11-10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/72.788651
Abstract: A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/72.737496
Abstract: Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent in the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results.
Publisher: ASME International
Date: 06-1997
DOI: 10.1115/1.2801247
Abstract: This paper deals with the position and force control for mechanical systems with holonomic constraints. Our concern is the design of a feedback controller such that the closed-loop system has a satisfactory transient response and is less sensitive to various types of disturbances. Using an appropriate transformation, the constrained system is converted into an unconstrained system of lower order. Then, an H∞, control problem involving the reduced system is formulated. In the case of state feedback, a systematic design procedure for solving the problem is presented, where the key step is the solution of an algebraic Riccati equation. An ex le is given to illustrate the effectiveness of the proposed method.
Publisher: IEEE
Date: 10-2013
Publisher: Elsevier BV
Date: 02-2002
Publisher: IEEE
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2000
DOI: 10.1109/81.852947
Publisher: Elsevier BV
Date: 09-1996
Publisher: Springer Science and Business Media LLC
Date: 10-12-2014
DOI: 10.1007/S10067-013-2447-4
Abstract: This study aims to assess mean signal intensity of cartilage on T1-weighted magnetic resonance imaging (MRI) images, and then examine whether mean signal intensity is associated with risk factors and measures of osteoarthritis in younger and older adults. A total of 50 younger adult subjects (mean age 41, range 29-57 64% female baseline only) and 168 older adult subjects (mean age 63, range 52-78 46% female baseline and 2.9 year followup) were randomly selected from the community. T1-weighted fat-supressed gradient recall echo MRI scans of right knees were performed. Image segmentation was performed semi-automatically, and measures of mean signal intensity and cartilage thickness for regions of cartilage were obtained. Urinary levels of C-terminal crosslinking telopeptide of type II collagen (U-CTX-II) were measured in younger adults. Cartilage defects were scored using a 5-point scale in both groups. In multivariable analyses, higher cartilage defects and BMI were significantly associated with lower same-region mean signal intensity in younger and older adults. CTX-II was negatively and significantly associated with mean signal intensity of cartilage in the lateral femoral and patellar sites. Joint space narrowing and osteophytes analysed in older adults were significantly associated with reduced mean signal intensity at various sites. Over 2.9 years, lower mean signal intensity at femoral and patellar sites and in whole knee was associated with decreases in cartilage thickness. Reduced mean signal intensity of cartilage on T1-weighted gradient recall echo MRI is associated with osteoarthritis risk factors and predicts cartilage loss suggesting low cartilage signal intensity may reflect early osteoarthritic changes.
Publisher: Elsevier BV
Date: 04-1996
Publisher: Institution of Engineering and Technology (IET)
Date: 04-2014
DOI: 10.1049/EL.2014.0466
No related grants have been discovered for Danchi Jiang.