44results about How to "Strong approach ability" patented technology

The invention discloses a gas concentration intelligent detection system, which is characterized in that: the system is composed of a gas environment parameter collection platform based on a wireless sensor network and a gas multi-level early warning module, and the gas environment parameter collection based on a wireless sensor network The platform realizes the detection of gas environment parameters, gas concentration adjustment and early warning. The gas multi-level early warning module is composed of a gas interval number neural network model, an interval number prediction model and an interval number GRNN neural network gas grade classifier; the invention effectively solves the problem of existing The gas concentration detection system does not detect the gas concentration according to the characteristics of non-linearity of gas changes, large hysteresis, and large working area and complex gas concentration changes, which greatly affects the accuracy of gas concentration detection.

The invention relates to an electroencephalographic and electromyographic information automatic intention recognition and upper limb intelligent control method and system, which are used for rehabilitation treatment of the upper limb of a stroke patient, an electroencephalographic and surface electromyographic signal collector collects and processes the electroencephalographic and surface electromyographic signals of the patient in real time, a mixed kernel function formed by weighting a polynomial kernel function and an RBF kernel function weights is used to perform fitting and prediction, soas to more accurately identify and monitor the motion intention of the patient, and judge the corresponding degree of rehabilitation, according to which a corresponding rehabilitation training strategy is adopted. When the rehabilitation degree of the upper limb of the stroke patient is low, passive training control is adopted. When the rehabilitation degree of the upper limb of the stroke patient is high, active, assisted and resistive control modes are adopted. The hybrid kernel function support vector machine model provided by the invention has better learning ability and generalization performance, high prediction accuracy and good control performance, and the prediction result meets the index requirements of a rehabilitation robot for stroke patients.