Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 8
items per page: 25 50 75
Sort by:

Abstract

The pharmacokinetics of a diclofenac sodium was investigated in swine. A single intravenous (i.v.) or intramuscular (i.m.) injection of 5% diclofenac sodium (concentration = 2.5 mg · kg-1) was administered to 8 healthy pigs according to a two-period crossover design. The pharmacokinetic parameters were calculated by non-compartmental analysis with DAS2.1.1 software. After a single i.v. administration, the main pharmacokinetic parameters of diclofenac sodium injection in swine were as follows: the elimination half-time (T1/2β) was 1.32±0.34 h; the area under the curve (AUC) was (55.50±5.50 μg · mL-1 h; the mean residence time (MRT) was 1.60±0.28 h; the apparent volume of distribution (Vd) was 0.50±0.05 L · kg-1; and the body clearance (CLB) was 0.26±0.04 L · (h · kg)-1. After the single i.m. administration, the pharmacokinetic parameters were as follows: peak time (Tmax) was 1.19±0.26 h; and peak concentration (Cmax) was 11.61±5.99 μg mL-1. The diclofenac sodium has the following pharmacokinetic characteristics in swine: rapid absorption and elimination; high peak concentration; and bioavailability.
Go to article

Abstract

Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.
Go to article

This page uses 'cookies'. Learn more