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The purpose of this paper is to present the static and dynamic characteristics of the rehabilitation joint.

The rehabilitation joint is driven by pneumatic muscle actuators (PMAs). Rehabilitation robot is normally composed of several rehabilitation joints. The static and dynamic characteristics of the rehabilitation joint are important for control of the rehabilitation robot. Analysis and modeling of the rehabilitation joint is based on experiments.

The static model of the PMA is obtained by the method of curve fitting and achieved better precision compared to the existing representative models. A second‐order model fits the dynamic characteristic of the rehabilitation joint better than a first order one.

The rehabilitation joint and the patient's joint combine to make an independent system, and the unstable factors of the patient's joint make it difficult in precisely modeling the rehabilitation joint.

The characteristics of the rehabilitation joint are all based on the data that were recorded in a series of with experiments, the same with modeling of the rehabilitation joint.

The purpose of this paper is to present the control methods of the exoskeleton robotic arm for stroke rehabilitation.

The robotic arm is driven by the pneumatic muscle actuators. The control system provides independent control for the robot. The joint axes of the robotic arm are arranged to mimic the natural upper limb workspace.

Findings are the classification of training modes and control methods of rehabilitation training, and the characters of both the instant spasm and the sustaining one.

This paper is a preliminary step in the control system and the kinematical characteristics should be analyzed to achieve high precision of movement.

Based on a hierarchical structure, the control system allows the execution of sequence of switching control methods: position, force, force/position and impedance. Patient‐active‐robot‐passive and patient‐passive‐robot‐active (PPRA) training modes are also presented in this paper. In PPRA mode, the robotic arm can provide pre‐specified resistances on the patient's arm. Both instant and sustaining spasms are taken into account for safety.

The pressurizer is one of the critical components in a nuclear reactor, whose main function is to maintain the primary system pressure within specific boundaries. The pressurizer is a large vessel that is made of carbon or low‐alloy steel shell, clad internally with a layer of stainless steel. It works with high temperature, high humidity and high pressure. The crack defects that would probably appear in the internal surface of the vessel must be detected and recorded, as well as changes to any existing known cracks. This paper presents an implementation of a mobile robot, which can sense and transmit the image of the internal surface to a remote analysis center, where the image is processed and recorded. A neural network‐based pattern classifier is employed to assist inspectors to detect the flaws that appear in the surface of the vessel. Because the real‐life defects rarely exist in the pressurizer, the training of the network becomes very difficult. A new algorithm is exploited to solve the problem. General considerations about the robot design are also presented.