This paper proposes an accelerometer-based gesture recognition algorithm. As a pre-process procedure, raw data output by accelerometer should be quantized, and then use discrete Hidden Markov Model to train and recognize them. Based upon this recognition algorithm, we treat gesture as a method of human-computer interaction and use it in 3D interaction subsystem in VR system named VDOM by following steps: establish Gesture-Semantic Map, train standard gestures, finally do recognition. Experimental results show that the system can recognize input gestures quickly with a reliable recognition rate. The users are able to perform most of the typical interaction tasks in virtual environment by this accelerometer-based device.
In order to solve the problem of accuracy and rapidity of short-term prediction of wind power output, the eXtreme Gradient Boosting (XGBoost) regression model is used in this paper to predict wind power output. For the models commonly used at the present stage, such as Long Short Term Memory (LSTM), random forest and ordinary XGBoost model, the modelling time is long, and the accuracy is not enough. In this paper, a genetic algorithm (GA) is introduced to improve the accuracy and speed of prediction of the XGBoost regression model. Firstly, the learning rate of the XGBoost model is optimized by using the good searching ability and flexibility of the genetic algorithm. Then variable weight combination prediction is carried out. The objective function for this problem is the mean square error that occurs between the value that is predicted and the value that actually occurs in the training set. GA is responsible for determining the model’s final weight. The historical output data of the wind plant is used in this paper to verify the XGBoost regression model based on a genetic algorithm and get the predicted value, which is then compared with the prediction results of LSTM and random forest algorithm. Example simulation and analysis show that the XGBoost regression model optimized by the genetic algorithm can be more significantly in solving the accuracy and rapidity of the prediction of short-term wind power output.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.