Deep imitation reinforcement learning for self-driving by vision

Deep reinforcement learning has achieved some remarkable results in self-driving.There is quite a lot of work to do in the area of autonomous driving with high real-time requirements because of the inefficiency of reinforcement learning in exploring large continuous motion spaces.A deep imitation reinforcement learning(DIRL)framework is presented to leam control policies of self-driving vehicles,which is based on a deep deterministic policy gradient algorithm(DDPG)by vision.The DIRL framework comprises two components,the perception module and the conttol module,using imitation learning(IL)and DDPG,respectively;The perception module employs the IL network as an encoder which processes an image into a low-dimensional feature vector.This vector is then delivered to the control module which outputs control commands.Meanwhile,the actor network of the DDPG is initialized with the trained IL network to improve exploration efficiency.In addition,a reward function for reinforcement learning is defined to improve the stability of self-driving vehicles,especially on curves.DIRL is verified by the open racing car simulator(TORCS),and the results show that the correct control strategy is learned successfully and has less training time.

Hyperchaotic Impulsive Synchronization and Digital Secure Communication

Based on chaos shift keying approach, impulsive signals from Hyperchaotic Chen system and Hyperchaotic Lü system are alternately emitted according to the transmission of binary signals “0” and “1”. In the receiver, these two hyperchaotic systems are adopted as response systems at the same time. The digital signals are recovered via comparing the discrete signals of the two error systems. Numerical simulations show the effectiveness of the method.

Speech Recognition for Parkinson’s Disease Based on Improved Genetic Algorithm and Data Enhancement Technology

Parkinson’s disease is one of the most destructive diseases to the nervous system.Speech disorder is one of the typical symptoms of Parkinson’s disease.Approximately 90%of Parkin-son’s patients develop some degree of speech disorder,which affects speech function faster than any other subsystem of the body.Screening Parkinson’s disease by sound is a very effective method that has attracted a growing number of researchers over the past decade.Patients with Parkinson’s disease could be identified by recording the sound signal of the pronunciation of words,extracting appropriate features and identifying the disturbance in their voices.This paper proposes an improved genetic algorithm combined with a data enhancement method for Parkinson’s speech signal recognition.Specifically,the methods first extract representative speech signal features through the L1 regularization SVM and then enhance the representative feature data by the SMOTE algorithm.Following this,both original and enhanced features are used to train an SVM classifier for speech signal recognition.An improved genetic algorithm was applied to find the optimal parameters of the SVM.The effectiveness of our proposed model is demonstrated by using Parkinson’s disease audio data set from the UCI machine learning library,and compared with the most advancedmethods,our proposed method has the best performance.