A Visual-Based Gesture Prediction Framework Applied in Social Robots

In daily life,people use their hands in various ways for most daily activities.There are many applications based on the position,direction,and joints of the hand,including gesture recognition,gesture prediction,robotics and so on.This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures.The model is applied to the NAO robot to verify the effectiveness of the proposed method.First of all,in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion,the Kalman filter is applied to the original data.Then some new feature descriptors are introduced.The length feature,angle feature and angular velocity feature are extracted from the filtered data.These features are fed into the long-short time memory recurrent neural network(LSTM-RNN)with different combinations.Experimental results show that the combination of coordinate,length and angle features achieves the highest accuracy of 99.31%,and it can also run in real time.Finally,the trained model is applied to the NAO robot to play the finger-guessing game.Based on the predicted gesture,the NAO robot can respond in advance.

Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor—the Case Study

This paper proposes a using Cellular-Based Vehicle Probe(CVP)at road-section(RS)method to detect and setup a model for traffic flow information(info)collection and monitor.There are multiple traffic collection devices including CVP,ETC-Based Vehicle Probe(EVP),Vehicle Detector(VD),and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem,monitor and control.The main project has been applied at Tai#2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018.This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory(LTSM)from recurrent neural network(RNN)model.We also provide a model verification and validation methodology with RNN for cross verification of system performance.

Double LSTM Structure for Network Traffic Flow Prediction

The network traffic prediction is important for service quality control in computer network.The performance of the traditional prediction method significantly degrades for the burst short-term flow.In view of the problem,this paper proposes a double LSTMs structure,one of which acts as the main flow predictor,another as the detector of the time the burst flow starts at.The two LSTM units can exchange information about their internal states,and the predictor uses the detector’s information to improve the accuracy of the prediction.A training algorithm is developed specially to train the structure offline.To obtain the prediction online,a pulse series is used as a simulant of the burst event.A simulation experiment is designed to test performance of the predictor.The results of the experiment show that the prediction accuracy of the double LSTM structure is significantly improved,compared with the traditional single LSTM structure.