A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

Correcting Image Distortion for Adaptive Cruise Control

One of the main drivers for intelligent transportation systems is safety. Adaptive cruise control, as a common solution for traffic safety, lias extended from radars to cameras. Due to high mobility of vehicles and unevenness of roads, the picture quality of cameras has been great challenges for camera-based adaptive cruise control. In this paper, an image distortion correction algorithm is addressed. Our method is based on optical flow technology which is normally applied in motion estimation and video compression research. We are the first to attempt to adapt it in image distortion correction. Two optical flow approaches, the Lucas-Kanade method and the Horn-Schunck method, are selected and compared. The procedure of image distortion correction using the optical flow method has been tested by both synthetic test images and camera images. The experimental results show that the Lucas-Kanade method is more suitable in the correction of image distortion.

Primary discussion on speech intelligibility of Chinese and the speech transmission index

The relation between the speech intelligibility of Chinese and the speech transmission index (STI)is discussed, which is based on some useful properties of the modulation transfer function (MTF)and the result obtained by articulation tests under different signal-to-noise ratios.