859 research outputs found
Adipokines – Toward the Molecular Dissection of Interactions Between Stromal Adipocytes and Breast Cancer Cells
published_or_final_versio
Modelling and control of an elastically joint-actuated cart-pole underactuated system
This paper investigates the modelling and closedloop tracking control issues of a novel elastic underactuated multibody system. A torsional inverted pendulum cart-pole system with a single rotary actuator at the pivot of the cart is proposed. The system dynamics which incorporates with motion planning is firstly described. An optimization procedure is then discussed to plan the feasible trajectories that not just meet the performance requirements but also obtain optimality with respect to the cart displacement and average velocity. A closed-loop tracking controller is designed under collocated partial feedback linearization (CPFL). Subsequent presentation of simulation demonstrates that the proposed system is promising as compared to the previous work. The paper concludes with the application of our novel scheme to the design and control of autonomous robot systems
Modelling, dynamic analysis and control of capsubot systems with stable propulsion for medical and recovery assistances
The growth of medical robots since the mid-1980s has been striking. From a few initial efforts in stereotactic brain surgery, orthopaedics, endoscopic surgery, microsurgery, and other areas, the field has expanded to include commercially marketed, clinically deployed systems, and a robust and exponentially expanding research community. Obscure gastrointestinal (GI) bleeding, Crohn disease, Celiac disease, small bower tumors, and other disorders that occur in the GI tract have always been challenging to be diagnosed and treated due to the inevitable difficulty in accessing such a complex environment within the human body. Robot-assisted minimally invasive surgery has become an choice
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
Geometric techniques for trajectory planning and chaos control of a bio-inspired autogenetic capsule robot
Biological systems achieve energy efficient and adaptive behaviours through extensive internal and external compliance interactions. Active dynamic compliance are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. The terminology bio-inspiration implies the understanding of fundamental principles underlying the motion behaviours of animals and humans and transfers these principles into the development of robotic systems. For example, during walking, the muscles constantly change their stiffness and damping when the leg is swinging forward and the foot is put on the ground. This idea enables the exploration in robotic systems with flexible elements—viscoelasticity to mimic the compliant motion of biological muscles. Underactuated systems with viscoelastic actuation are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment
On periodically pendulum-diven systems for underactuated locomotion: a viscoelastic jointed model
This paper investigates the locomotion principles and nonlinear dynamics of the periodically pendulum-driven (PD) systems using the case of a 2-DOF viscoelastic jointed model. As a mechanical system with underactuation degree one, the proposed system has strongly coupled nonlinearities and can be utilized as a potential benchmark for studying complicated PD systems. By mathematical modeling and non-dimensionalization of the physical system, an insight is obtained to the global system dynamics. The proposed 2-DOF viscoelastic jointed model establishes a commendable interconnection between the system dynamics and the periodically actuated force. Subsequently, the periodic locomotion principles of the actuated subsystem are elaborately studied and synthesized with the characteristic of viscoelastic element. Then the analysis of qualitative changes is conducted respectively under the varying excitation amplitude and frequency. Simulation results validate the efficiency and performance of the proposed system comparing with the conventional system
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